El valor de p del test no es un ‘índice matemático’, es simplemente una frecuencia relativa / The p-value of the test is not a ‘mathematical index’, it is simply a relative frequency

Las revistas científicas más importantes en campos como medicina, biología y sociología publican reiteradamente artículos y editoriales denunciando que un gran porcentaje de médicos no entiende los conceptos básicos del análisis estadístico, lo que favorece el riesgo de cometer errores al interpretar los datos, los hace más vulnerables frente a informaciones falsas y reduce la eficacia de la investigación. Este problema se extiende a lo largo de toda su carrera profesional y se debe, en gran parte, a una enseñanza deficiente en estadística que es común en países desarrollados. En palabras de H. Halle y S. Krauss, ‘el 90% de los profesores universitarios alemanes que usan con asiduidad el valor de p de los test no entiende lo que mide ese valor’. Es importante destacar que los razonamientos básicos del análisis estadístico son similares a los que realizamos en nuestra vida cotidiana y que comprender los conceptos básicos del análisis estadístico no requiere conocimiento matemático alguno. En contra de lo que muchos investigadores creen, el valor de p del test no es un ‘índice matemático’ que nos permita concluir claramente si, por ejemplo, un fármaco es más efectivo que el placebo. El valor de p del test es simplemente un porcentaje.(AU)

Abstract. Leading scientific journals in fields such as medicine, biology and sociology repeatedly publish articles and editorials claiming that a large percentage of doctors do not understand the basics of statistical analysis, which increases the risk of errors in interpreting data, makes them more vulnerable to misinformation and reduces the effectiveness of research. This problem extends throughout their careers and is largely due to the poor training they receive in statistics – a problem that is common in developed countries. As stated by H. Halle and S. Krauss, ‘90% of German university lecturers who regularly use the p-value in tests do not understand what that value actually measures’. It is important to note that the basic reasoning of statistical analysis is similar to what we do in our daily lives and that understanding the basic concepts of statistical analysis does not require any knowledge of mathematics. Contrary to what many researchers believe, the p-value of the test is not a ‘mathematical index’ that allows us to clearly conclude whether, for example, a drug is more effective than a placebo. The p-value of the test is simply a percentage.(AU)

Spatial distribution and hydrogeochemical evaluations of groundwater and its suitability for drinking and irrigation purposes in kaligonj upazila of satkhira district of Bangladesh.

Groundwater is a significant water resource for drinking and irrigation in Satkhira district, Bangladesh. The depletion of groundwater resources and deterioration in its quality are the results of the confluence of factors such as industrialization, intensive irrigation, and rapid population growth. For this reason, this study focused on the evaluation of tubewell water of six unions of Kaligonj upazila in Satkhira district, which is situated in the coastal southwest part of Bangladesh. Major and trace elemental concentrations were assimilated into positive matrix factorization (PMF) to identify potential sources and their respective contributions. Principal component analysis (PCA) revealed that groundwater salinization and manmade activities were the primary causes of heavy metals in the coastal groundwater. Its average pH value was found to be 7.5, while Dissolved oxygen, Total dissolved solids, salinity, and conductivity, with values ranging from 1.18 to 7.38 mg/L, 0.5-4.88 g/L, 0.4-5%, and 0.95 to 8.56 mS/cm, respectively. The total hardness average value was 561.7 mg/L, classified into the very hard water categories, which is why 90% of the tubewell water samples were unfit for household purposes. All samples had an excessive level of arsenic present. The iron concentration of fifteen (15) samples crossed the standard limit according to WHO 2011 value. Around 63% of the samples were of the Na+-K+-Cl--SO42- type, and about 72% were sodium-potassium and alkali types. 98% of samples were covered in chloride and bicarbonate. The findings showed that 45.83% of the groundwater samples had negative Chloroalkaline index (CAIs), while 54.16% had positive. The permeability index (PI) was an average of 73%, and residual sodium carbonate (RSC) averaged 260.2 mg/L, and the findings clearly showed that 80% of the samples weren't appropriate for irrigation. According to the sodium adsorption ratio (SAR) value, 65% of the samples fell into the unsuitable category. These calculations indicated a high overall salinity hazard in the study area, which may be caused by the intrusion of sea water given that the study area is close to the coastal region. Findings compared to standards revealed that the majority of the samples were deemed unfit for drinking and irrigation purposes. Hence, additional attention must be paid to this area to ensure the availability of drinkable water and to preserve sustainable farming practices.

Resting network architecture of theta oscillations reflects hyper-learning of sensorimotor information in Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome is a neurodevelopmental disorder characterized by motor and vocal tics. It is associated with enhanced processing of stimulus-response associations, including a higher propensity to learn probabilistic stimulus-response contingencies (i.e. statistical learning), the nature of which is still elusive. In this study, we investigated the hypothesis that resting-state theta network organization is a key for the understanding of superior statistical learning in these patients. We investigated the graph-theoretical network architecture of theta oscillations in adult patients with Gilles de la Tourette syndrome and healthy controls during a statistical learning task and in resting states both before and after learning. We found that patients with Gilles de la Tourette syndrome showed a higher statistical learning score than healthy controls, as well as a more optimal (small-world-like) theta network before the task. Thus, patients with Gilles de la Tourette syndrome had a superior facility to integrate and evaluate novel information as a trait-like characteristic. Additionally, the theta network architecture in Gilles de la Tourette syndrome adapted more to the statistical information during the task than in HC. We suggest that hyper-learning in patients with Gilles de la Tourette syndrome is likely a consequence of increased sensitivity to perceive and integrate sensorimotor information leveraged through theta oscillation-based resting-state dynamics. The study delineates the neural basis of a higher propensity in patients with Gilles de la Tourette syndrome to pick up statistical contingencies in their environment. Moreover, the study emphasizes pathophysiologically endowed abilities in patients with Gilles de la Tourette syndrome, which are often not taken into account in the perception of this common disorder but could play an important role in destigmatization.

Assessing the feasibility of statistical inference using synthetic antibody-antigen datasets.

Simulation frameworks are useful to stress-test predictive models when data is scarce, or to assert model sensitivity to specific data distributions. Such frameworks often need to recapitulate several layers of data complexity, including emergent properties that arise implicitly from the interaction between simulation components. Antibody-antigen binding is a complex mechanism by which an antibody sequence wraps itself around an antigen with high affinity. In this study, we use a synthetic simulation framework for antibody-antigen folding and binding on a 3D lattice that include full details on the spatial conformation of both molecules. We investigate how emergent properties arise in this framework, in particular the physical proximity of amino acids, their presence on the binding interface, or the binding status of a sequence, and relate that to the individual and pairwise contributions of amino acids in statistical models for binding prediction. We show that weights learnt from a simple logistic regression model align with some but not all features of amino acids involved in the binding, and that predictive sequence binding patterns can be enriched. In particular, main effects correlated with the capacity of a sequence to bind any antigen, while statistical interactions were related to sequence specificity.

Unraveling Temporal Dynamics of Multidimensional Statistical Learning in Implicit and Explicit Systems: An X-Way Hypothesis.

Statistical learning enables humans to involuntarily process and utilize different kinds of patterns from the environment. However, the cognitive mechanisms underlying the simultaneous acquisition of multiple regularities from different perceptual modalities remain unclear. A novel multidimensional serial reaction time task was developed to test 40 participants' ability to learn simple first-order and complex second-order relations between uni-modal visual and cross-modal audio-visual stimuli. Using the difference in reaction times between sequenced and random stimuli as the index of domain-general statistical learning, a significant difference and dissociation of learning occurred between the initial and final learning phases. Furthermore, we used a negative and positive occurrence-frequency-and-reaction-time correlation to indicate implicit and explicit learning, respectively, and found that learning simple uni-modal patterns involved an implicit-to-explicit segue, while acquiring complex cross-modal patterns required an explicit-to-implicit segue, resulting in a X-shape crossing of regularity learning. Thus, we propose an X-way hypothesis to elucidate the dynamic interplay between the implicit and explicit systems at two distinct stages when acquiring various regularities in a multidimensional probability space.

Statistically derived proxy potentials accelerate geometry optimization of crystal structures.

The crystal structures of known materials contain the information about the interatomic interactions that produced these stable compounds. Similar to the use of reported protein structures to extract effective interactions between amino acids, that has been a useful tool in protein structure prediction, we demonstrate how to use this statistical paradigm to learn the effective inter-atomic interactions in crystalline inorganic solids. By analyzing the reported crystallographic data for inorganic materials, we have constructed statistically derived proxy potentials (SPPs) that can be used to assess how realistic or unusual a computer-generated structure is compared to the reported experimental structures. The SPPs can be directly used for structure optimization to improve this similarity metric, that we refer to as the SPP score. We apply such optimization step to markedly improve the quality of the input crystal structures for DFT calculations and demonstrate that the SPPs accelerate geometry optimization for three systems relevant to battery materials. As this approach is chemistry-agnostic and can be used at scale, we produced a database of all possible pair potentials in a tabulated form ready to use.

Breaking down barriers: promoting journals beyond the page with open access journal clubs.

In 2020, during the early days of the COVID-19 pandemic, the British Journal of Psychiatry (BJPsych) established a series of free online teaching sessions called BJPsych Journal Clubs. Their educational purpose is two-fold: (a) to provide junior psychiatrists with a friendly but large-scale platform to evaluate and critically appraise recent articles published in the BJPsych and (b) to present new research findings in an open and accessible manner. In this paper, we discuss our framework, the challenges we encountered, how the original model is evolving based on feedback from trainees, and tips for success when delivering international online journal clubs.

Scan-Free and Fully Automatic Tracking of Native Knee Anatomy from Dynamic Stereo-Radiography with Statistical Shape and Intensity Models.

Kinematic tracking of native anatomy from stereo-radiography provides a quantitative basis for evaluating human movement. Conventional tracking procedures require significant manual effort and call for acquisition and annotation of subject-specific volumetric medical images. The current work introduces a framework for fully automatic tracking of native knee anatomy from dynamic stereo-radiography which forgoes reliance on volumetric scans. The method consists of three computational steps. First, captured radiographs are annotated with segmentation maps and anatomic landmarks using a convolutional neural network. Next, a non-convex polynomial optimization problem formulated from annotated landmarks is solved to acquire preliminary anatomy and pose estimates. Finally, a global optimization routine is performed for concurrent refinement of anatomy and pose. An objective function is maximized which quantifies similarities between masked radiographs and digitally reconstructed radiographs produced from statistical shape and intensity models. The proposed framework was evaluated against manually tracked trials comprising dynamic activities, and additional frames capturing a static knee phantom. Experiments revealed anatomic surface errors routinely below 1.0 mm in both evaluation cohorts. Median absolute errors of individual bone pose estimates were below 1.0 ∘ or mm for 15 out of 18 degrees of freedom in both evaluation cohorts. Results indicate that accurate pose estimation of native anatomy from stereo-radiography may be performed with significantly reduced manual effort, and without reliance on volumetric scans.

Digital application for drug product potency target evaluation in biopharmaceutical manufacturing.

Biopharmaceutical manufacturing entails a series of highly regulated steps. The manufacturing of safe and efficacious drug product (DP) requires testing of critical quality attributes (CQAs) against specification limits. DP potency concentration, which measures the dosage strength of a particular DP, is a CQA of great interest. In order to minimize the DP potency out-of-specification (OOS) risk, sterile fill finish (SFF) process adjustments may be needed. Varying the potency targets can be one such process adjustment. To facilitate such evaluation, data acquisition and statistical calculations are required. Regularly conducting the OOS risk assessment manually using commercial statistical software can be tedious, error-prone, and impractical, especially when several alternate potency targets are under consideration. In this work, the development of a novel framework for OOS risk assessment and deployment of cloud-based statistical software application to facilitate the risk assessment are presented. This application is intended to streamline the assessment of alternate potency targets for DP in biologics manufacturing. The major aspects of this potency targeting application development are presented in detail. Specifically, data sources, pipeline, application architecture, back-end and front-end development as well as application verification are discussed. Finally, several use cases are presented to highlight the application's utility in biologics manufacturing.

Material flow analysis and statistical entropy evaluation of plastic packaging for express delivery in China.

Encouraging the recycling of plastic packaging materials in express delivery is a necessary step toward environmentally friendly industrial development. In this study, we present a framework for analyzing the flow of materials in express plastic packaging, from production and manufacturing to consumption and recycling. In examining the use of recycled materials in post-consumer express plastic packaging and the destination of consumer packaging waste in 2020 and 2021, we found that 44.4% (1613.6 Gg) of the studied express plastic packaging was incinerated. Additionally, approximately 1296.6 Gg of express plastic packaging flowed into rural areas. Our calculations showed that the ΔRSE in 2020 was 15.1%, and on the condition that 25% separated collection with 80% recycling, ΔRSE would be - 0.5%. Results verified that separated collection is an important step in the recycling strategy for packaging materials. Survey data from universities in Beijing indicate that currently, 26% of college students are participating in the separate collection of packaging.

Brain 18 F-FDG PET reveals cortico-subcortical hypermetabolic dysfunction in juvenile neuropsychiatric systemic lupus erythematosus.

BACKGROUND: In juvenile systemic lupus erythematosus (j-SLE) with neuropsychiatric (NP) symptoms, there is a lack of diagnostic biomarkers. Thus, we study whether PET-FDG may identify any metabolic dysfunction in j-NPSLE. METHODS: A total of 19 18FDG-PET exams were consecutively performed using PET-MRI system in 11 non-sedated patients presenting with j-NPSLE (11-18y) for less than 18 months (m) and without any significant lesion at MRI. Psychiatric symptoms were scored from 0 (none) to 3 (severe) at PET time. PET images were visually analyzed and voxel-based analyses of cerebral glucose metabolism were performed using statistical parametric mapping (spm) with an age-matched control group, at threshold set > 50 voxels using both p < 0.001 uncorrected (unc.) and p < 0.05 corrected family wise error (FWE). RESULTS: Patients exhibited mainly psychiatric symptoms, with diffuse inflammatory j-NPSLE. First PET (n = 11) was performed at a mean of 15y of age, second/third PET (n = 7/n = 1) 6 to 19 m later. PET individual analysis detected focal bilateral anomalies in 13/19 exams visually but 19/19 using spm (unc.), mostly hypermetabolic areas (18/19). A total of 15% of hypermetabolic areas identified by spm had been missed visually. PET group analysis (n = 19) did not identify any hypometabolic area, but a large bilateral cortico-subcortical hypermetabolic pattern including, by statistical decreasing order (unc.), thalamus, subthalamic brainstem, cerebellum (vermis and cortex), basal ganglia, visual, temporal and frontal cortices. Mostly the subcortical hypermetabolism survived to FWE analysis, being most intense and extensive (51% of total volume) in thalamus and subthalamus brainstem. Hypermetabolism was strictly subcortical in the most severe NP subgroup (n = 8, scores 2-3) whereas it also extended to cerebral cortex, mostly visual, in the less severe subgroup (n = 11, scores 0-1), but difference was not significant. Longitudinal visual analysis was inconclusive due to clinical heterogeneity. CONCLUSIONS: j-NPSLE patients showed a robust bilateral cortico-subcortical hypermetabolic network, focused subcortically, particularly in thalamus, proportionally to psychiatric features severity. Further studies with larger, but homogeneous, cohorts are needed to determine the sensitivity and specificity of this dysfunctional pattern as a potential biomarker in diffuse inflammatory j-NPSLE with normal brain MRI.

Implications of variability on medical device chemical equivalence assessment.

Chemical equivalence testing can be used to assess the biocompatibility implications of a materials or manufacturing change for a medical device. This testing can provide a relatively facile means to evaluate whether the change may result in additional or different toxicological concerns. However, one of the major challenges in the interpretation of chemical equivalence data is the lack established criteria for determining if two sets of extractables data are effectively equivalent. To address this gap, we propose a two-part approach based upon a relatively simple statistical model. First, the probability of a false positive conclusion, wherein there is an incorrectly perceived increase for a given analyte in the comparator relative to the baseline device, can be reduced to a prescribed level by establishing an appropriate acceptance criterion for the ratio of the observed means. Second, the probability of a false negative conclusion, where an actual increase in a given analyte cannot be discerned from the test results, can be minimized by specifying a limiting value of applicability based on the margin of safety (MoS) of the analyte. This approach provides a quantitative, statistically motivated method to interpret chemical equivalence data, despite the relatively high intrinsic variability and small number of replicates typically associated with a chemical characterization evaluation.

Spatiotemporal variations, health risk assessment, and sources of potentially toxic elements in potamic water of the Anday Stream Basin (Türkiye), Black Sea Region.

Monitoring and protecting freshwater habitats are paramount for a sustainable water management perspective. This study investigated potentially toxic elements (PTEs) in the potamic water of the Anday Stream Basin (Türkiye), Black Sea Region, for a hydrological year (from May 2020 to April 2021). Among PTEs, the highest average values were recorded for sodium (Na) at 41.3 mg/L and the lowest for mercury (Hg) at 0.009 µg/L and noted under quality guidelines. The stream was found to be at the level of "Low Heavy Metal Pollution" and "Low Contamination" based on the ecotoxicological risk indices. The highest calculated hazard quotient (HQ) value of 1.21E-02 for Cd was noted in the children via the dermal pathway and the lowest of 6.91E-06 for Fe in adults via the ingestion pathway. Results revealed a higher hazard index (HI) value of 1.50E-02 for Cd to children and the lowest of 1.98E-05 for Fe to adults. As a result of applying agricultural risk indices, the stream showed sodium adsorption ratio values less than 6 and was found to be "Excellent" for agriculture. However, the sodium percentage values were less than 20 and found "Permissible" and the magnesium hazard > 50 and noted as "Unsuitable" for agriculture. Statistical analysis revealed that natural factors mainly attributed to PTE contamination of the Anday Stream Basin.

Assessing the Impact of Precancerous Lesions on Oral Health-Related Quality of Life: A Study at Tertiary Care Hospital.

Objective: The aim of this cross-sectional study was to assess the oral health-related quality of life (OHRQoL) in a cohort of 500 patients diagnosed with precancerous lesions and conditions at a tertiary care hospital in Central India. Methods: 500 patients with confirmed precancerous oral lesions and conditions were recruited for the study. The Oral Health Impact Profile (OHIP-14) questionnaire, a validated instrument consisting of 14 items, was used to assess the OHRQoL of the participants. The OHIP-14 questionnaire scores were statistically analyzed using appropriate methods. Results: The majority of the participants were with a mean age of 48 years. The mean OHIP-14 score was calculated to be 45.1, indicating the overall impact of oral health on the quality of life of patients with precancerous lesions and conditions. The individual domains most affected were functional limitations and physical pain. Conclusion: This study demonstrates that promoting oral health awareness and regular screenings in the community is needed to prevent the progression of oral precancerous conditions and ultimately reduce the burden of oral cancer.

Insights into the key odorants in fresh and dried Amomum tsaoko using the sensomics approach.

To identify the key odorants in Amomum tsaoko (AT), volatiles in fresh AT (FAT) and dried AT (DAT) were investigated using molecular sensory science. In addition to this, the sensomics approach was used to confirm the presence of the compound in FAT that contributed the most to its aroma profile. A total of 49 odor-active compounds (43 in FAT and 42 in DAT) with flavor dilution (FD) factors ranging from 1 to 6561 were identified, with eucalyptol exhibiting the highest FD factor of 6561. Odorants with FD factors ≥ 27 were quantitated, and 23 and 20 compounds in FAT and DAT, respectively, with odor activity value ≥ 1 were determined as key odorants. Recombination and omission experiment further indicated that (E)-2-dodecenal, geranial, octanal, (E)-2-octenal, (E)-2-decenal, and eucalyptol contributed significantly to the overall aroma profile of FAT. After drying of FAT, the concentrations of aldehydes decreased significantly, whereas those of terpene hydrocarbons increased. Multivariate statistical analysis revealed that 26 FAT and 23 DAT odorants were biomarker compounds.

Integration and holistic analysis of multiple multidimensional soil data sets.

Complex matrices such as soil have a range of measurable characteristics, and thus data to describe them can be considered multidimensional. These characteristics can be strongly influenced by factors that introduce confounding effects that hinder analyses. Traditional statistical approaches lack the flexibility and granularity required to adequately evaluate such matrices, particularly those with large dataset of varying data types (i.e. quantitative non-compositional, quantitative compositional). We present a statistical workflow designed to effectively analyse complex, multidimensional systems, even in the presence of confounding variables. The developed methodology involves exploratory analysis to identify the presence of confounding variables, followed by data decomposition (including strategies for both compositional and non-compositional quantitative data) to minimise the influence of these confounding factors such as sampling site/location. These data processing methods then allow for common patterns to be highlighted in the data, including the identification of biomarkers and determination of non-trivial associations between variables. We demonstrate the utility of this statistical workflow by jointly analysing the chemical composition and fungal biodiversity of New Zealand vineyard soils that have been managed with either organic low-input or conventional input approaches. By applying this pipeline, we were able to identify biomarkers that distinguish viticultural soil from both approaches and also unearth links and associations between the chemical and metagenomic profiles. While soil is an example of a system that can require this type of statistical methodology, there are a range of biological and ecological systems that are challenging to analyse due to the complex interplay of global and local effects. Utilising our developed pipeline will greatly enhance the way that these systems can be studied and the quality and impact of insight gained from their analysis.

Interlimb kinetic asymmetries during the tuck jump assessment are more exposed following kinetic stabilization.

OBJECTIVE: To analyse interlimb kinetics and asymmetries during the tuck jump assessment (TJA), before and after kinetic stabilization, to identify injury risk in healthy female athletes. DESIGN: Cross-sectional study. SETTING: Laboratory. PARTICIPANTS: Twenty-five healthy females (age 21.0 ± 1.83 yrs; height 1.68 ± 0.06 m; body mass 69.4 ± 10.7 kg). MAIN OUTCOME MEASURES: Kinetics were measured during 10-s trials of the TJA and absolute asymmetries compared, before and after kinetic stabilization using paired sample t-tests. Statistical parametric mapping (SPM) compared vertical ground reaction force (VGRF) data for each limb during the jumping cycles before and after stabilization. RESULTS: Small to moderate increases in interlimb asymmetries were observed after stabilization for VGRF, relative vertical leg stiffness, average loading rate, total and propulsive impulse, peak braking and propulsive force (p < 0.05). SPM revealed significant interlimb differences between 77-98% and 83-99% of ground contact for the jumping cycles pre- and post-stabilization respectively. CONCLUSIONS: Larger asymmetries were evident after kinetic stabilization, with increased VGRF in the non-dominant limb. We speculate that participants sacrificed interlimb landing symmetry to achieve kinetic stability, which may reflect a primal landing strategy that forgoes movement quality. Assessing lower limb biomechanics using the TJA should involve examining kinetic stability and interlimb kinetic asymmetries.

Modeling study on oil spill transport in the Great Lakes: The unignorable impact of ice cover.

The rise in oil trade and transportation has led to a continuous increase in the risk of oil spills, posing a serious worldwide concern. However, there is a lack of numerical models for predicting oil spill transport in freshwater, especially under icy conditions. To tackle this challenge, we developed a prediction system for oil with ice modeling by coupling the General NOAA Operational Modeling Environment (GNOME) model with the Great Lakes Operational Forecast System (GLOFS) model. Taking Lake Erie as a pilot study, we used observed drifter data to evaluate the performance of the coupled model. Additionally, we developed six hypothetical oil spill cases in Lake Erie, considering both with and without ice conditions during the freezing, stable, and melting seasons spanning from 2018 to 2022, to investigate the impacts of ice cover on oil spill processes. The results showed the effective performance of the coupled model system in capturing the movements of a deployed drifter. Through ensemble simulations, it was observed that the stable season with high-concentration ice had the most significant impact on limiting oil transport compared to the freezing and melting seasons, resulting in an oil-affected open water area of 49 km2 on day 5 with ice cover, while without ice cover it reached 183 km2. The stable season with high-concentration ice showed a notable reduction in the probability of oil presence in the risk map, whereas this reduction effect was less prominent during the freezing and melting seasons. Moreover, negative correlations between initial ice concentration and oil-affected open water area were consistent, especially on day 1 with a linear regression R-squared value of 0.94, potentially enabling rapid prediction. Overall, the coupled model system serves as a useful tool for simulating oil spills in the world's largest freshwater system, particularly under icy conditions, thus enhancing the formulation of effective emergency response strategies.

Performance and micro-pacing in sprint cross-country skiing: A comparison of individual time-trial and head-to-head race formats.

This study compared performance strategies and sub-technique selection in cross-country skate skiing sprint races, specifically individual time-trial (ITT) and head-to-head (H2H) formats. Fourteen male cross-country skiers from the Chinese national team participated in the FIS-sanctioned sprint race day. GNSS and heart rate sensors recorded positioning, skiing speeds, heart rate, sub-technique usage, and skiing kinematics. Statistical parametric mapping (SPM) was used to determine the course positions (clusters) where instantaneous skiing speed was significantly associated with section time. One-way analyses of variance were used to examine differences between the ITT and H2H. H2H race speeds were 2.4 ± 0.2% faster than the ITT race (p < 0.05).Variations in sub-technique and skiing kinematics were observed between race formats, indicating different strategies and tactics employed by athletes. SPM identified specific clusters (primarily uphill) where the fastest athlete gained significant time over the slowest. The greatest time gains were associated with higher G3 sub-technique usage and longer G3 cycle length on steep uphill terrain (9-13% gradients). Integrating SPM analyses and sub-technique assessments can help optimise performance and tactics in sprint races. This study enhances our understanding of cross-country skiing dynamics and performance variations among elite competitors.