Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Towards a general-purpose foundation model for computational pathology.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

Recurrent central odontogenic fibroma in a patient with nevoid basal cell carcinoma syndrome: case report and in vitro analysis.

OBJECTIVE: Central odontogenic fibromas (COF) are rare, benign tumors derived from dental mesenchymal tissue that may occur in the maxilla or mandible. This report describes primary and recurrent COF in the mandible of a patient with nevoid basal cell carcinoma syndrome (NBCCS). STUDY DESIGN: A 36-year-old African American male presented with a COF and its recurrence 17 months later. Tissue pieces were obtained from both occurrences with IRB-approved signed consent. Collected tissue pieces were dissected; one portion was formalin-fixed and paraffin-embedded, and the other was cultured for the isolation of cell populations from the primary (COdF-1) and recurrent (COdF-1a) tumors. Quantification real-time polymerase chain reaction (qRT-PCR), immunohistochemistry, and DNA sequencing were used for gene and protein analysis of the primary tumor and cell populations. RESULTS: Histopathologic analysis of the tumor showed sparse odontogenic epithelial cords in fibrous connective tissue, and qRT-PCR analysis of tumor and cell populations (COdF-1 and COdF-1a) detected VIM, CK14, CD34, CD99 and ALPL mRNA expression. Protein expression was confirmed by immunohistochemistry. CD34 expression in primary tissues was higher than in tumor cells due to tumor vascularization. DNA sequencing indicated the patient had PTCH1 mutations. CONCLUSIONS: Histopathology, mRNA, and protein expression indicate the rare occurrence of COF in a patient with mutated PTCH1 gene and NBCCS.

Discovery, Optimization, and Biological Evaluation of Arylpyridones as Cbl-b Inhibitors.

Casitas B-lymphoma proto-oncogene-b (Cbl-b), a member of the Cbl family of RING finger E3 ubiquitin ligases, has been demonstrated to play a central role in regulating effector T-cell function. Multiple studies using gene-targeting approaches have provided direct evidence that Cbl-b negatively regulates T, B, and NK cell activation via a ubiquitin-mediated protein modulation. Thus, inhibition of Cbl-b ligase activity can lead to immune activation and has therapeutic potential in immuno-oncology. Herein, we describe the discovery and optimization of an arylpyridone series as Cbl-b inhibitors by structure-based drug discovery to afford compound 31. This compound binds to Cbl-b with an IC50 value of 30 nM and induces IL-2 production in T-cells with an EC50 value of 230 nM. Compound 31 also shows robust intracellular target engagement demonstrated through inhibition of Cbl-b autoubiquitination, inhibition of ubiquitin transfer to ZAP70, and the cellular modulation of phosphorylation of a downstream signal within the TCR axis.

Risk factors for surgical site infection after surgical treatment of closed distal radial fractures.

We assessed operatively treated closed distal radial fractures to identify independent risk factors for surgical site infection after treatment. A retrospective review was carried out of 531 operatively treated closed distal radial fractures over a 5-year period. Multiple logistic regression was performed with infection as the dependent variable, using a stepwise regression procedure to select variables to construct the final model. In total, 19 (3.6%) fractures were complicated by postoperative surgical site infection. Uncontrolled diabetes with HbA1c >7, the presence of external fixation or external Kirschner wires, and tobacco use were significant independent predictors of infection. Age and time in the operating room were also statistically significant predictors but deemed to be not clinically meaningful.Level of evidence: IV.

Comparison of ChatGPT-3.5, ChatGPT-4, and Orthopaedic Resident Performance on Orthopaedic Assessment Examinations.

INTRODUCTION: Artificial intelligence (AI) programs have the ability to answer complex queries including medical profession examination questions. The purpose of this study was to compare the performance of orthopaedic residents (ortho residents) against Chat Generative Pretrained Transformer (ChatGPT)-3.5 and GPT-4 on orthopaedic assessment examinations. A secondary objective was to perform a subgroup analysis comparing the performance of each group on questions that included image interpretation versus text-only questions. METHODS: The ResStudy orthopaedic examination question bank was used as the primary source of questions. One hundred eighty questions and answer choices from nine different orthopaedic subspecialties were directly input into ChatGPT-3.5 and then GPT-4. ChatGPT did not have consistently available image interpretation, so no images were directly provided to either AI format. Answers were recorded as correct versus incorrect by the chatbot, and resident performance was recorded based on user data provided by ResStudy. RESULTS: Overall, ChatGPT-3.5, GPT-4, and ortho residents scored 29.4%, 47.2%, and 74.2%, respectively. There was a difference among the three groups in testing success, with ortho residents scoring higher than ChatGPT-3.5 and GPT-4 ( P < 0.001 and P < 0.001). GPT-4 scored higher than ChatGPT-3.5 ( P = 0.002). A subgroup analysis was performed by dividing questions into question stems without images and question stems with images. ChatGPT-3.5 was more correct (37.8% vs. 22.4%, respectively, OR = 2.1, P = 0.033) and ChatGPT-4 was also more correct (61.0% vs. 35.7%, OR = 2.8, P < 0.001), when comparing text-only questions versus questions with images. Residents were 72.6% versus 75.5% correct with text-only questions versus questions with images, with no significant difference ( P = 0.302). CONCLUSION: Orthopaedic residents were able to answer more questions accurately than ChatGPT-3.5 and GPT-4 on orthopaedic assessment examinations. GPT-4 is superior to ChatGPT-3.5 for answering orthopaedic resident assessment examination questions. Both ChatGPT-3.5 and GPT-4 performed better on text-only questions than questions with images. It is unlikely that GPT-4 or ChatGPT-3.5 would pass the American Board of Orthopaedic Surgery written examination.

A General-Purpose Self-Supervised Model for Computational Pathology.

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.

Longitudinal Assessment of Modern Spine Surgery Training: 10-Year Follow-up of a Nationwide Survey of Residency and Spine Fellowship Program Directors.

Spine surgeons complete training through residency in orthopaedic surgery (ORTH) or neurosurgery (NSGY). A survey was conducted in 2013 to evaluate spine surgery training. Over the past decade, advances in surgical techniques and the changing dynamics in fellowship training may have affected training and program director (PD) perceptions may have shifted. Methods: This study is a cross-sectional survey distributed to all PDs of ORTH and NSGY residencies and spine fellowships in the United States. Participants were queried regarding characteristics of their program, ideal characteristics of residency training, and opinions regarding the current training environment. χ2 tests were used to compare answers over the years. Results: In total, 241 PDs completed the survey. From 2013 to 2023, NSGY increased the proportion of residents with >300 spine cases (86%-100%) while ORTH remained with >90% of residents with < 225 cases (p < 0.05). A greater number of NSGY PDs encouraged spine fellowship even for community spine surgery practice (0% in 2013 vs. 14% in 2023, p < 0.05), which continued to be significantly different from ORTH PDs (∼88% agreed, p > 0.05). 100% of NSGY PDs remained confident in their residents performing spine surgery, whereas ORTH confidence significantly decreased from 43% in 2013 to 25% in 2023 (p < 0.05). For spinal deformity, orthopaedic PDs (92%), NSGY PDs (96%), and fellowship directors (95%), all agreed that a spine fellowship should be pursued (p = 0.99). In both 2013 and 2023, approximately 44% were satisfied with the spine training model in the United States. In 2013, 24% of all PDs believed we should have a dedicated spine residency, which increased to 39% in 2023 (fellowship: 57%, ORTH: 38%, NSGY: 21%) (p < 0.05). Conclusion: Spine surgery training continues to evolve, yet ORTH and neurological surgery training remains significantly different in case volumes and educational strengths. In both 2013 and 2023, less than 50% of PDs were satisfied with the current spine surgery training model, and a growing minority believe that spine surgery should have its own residency training pathway. Level of Evidence: IV.

Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples.

Human tissue consists of complex structures that display a diversity of morphologies, forming a tissue microenvironment that is, by nature, three-dimensional (3D). However, the current standard-of-care involves slicing 3D tissue specimens into two-dimensional (2D) sections and selecting a few for microscopic evaluation1,2, with concomitant risks of sampling bias and misdiagnosis3-6. To this end, there have been intense efforts to capture 3D tissue morphology and transition to 3D pathology, with the development of multiple high-resolution 3D imaging modalities7-18. However, these tools have had little translation to clinical practice as manual evaluation of such large data by pathologists is impractical and there is a lack of computational platforms that can efficiently process the 3D images and provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images from diverse imaging modalities and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy12-14 or microcomputed tomography15,16 and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves an area under the receiver operating characteristic curve (AUC) of 0.86 and 0.74, superior to 2D traditional single-slice-based prognostication (AUC of 0.79 and 0.57), suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting that there is value in capturing larger extents of spatially heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology techniques by researchers and clinicians, MAMBA provides a general and efficient framework for 3D weakly supervised learning for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.

Psychological impact of the COVID-19 pandemic on breast cancer patients.

PURPOSE: The COVID-19 pandemic uniquely impacted patients with breast cancer as mastectomies were allowed to proceed, yet breast reconstruction surgeries were halted. The purpose of this study was to examine the effect of the COVID-19 pandemic on the rates of breast reconstruction and patients' well-being. METHODS: A chart review included all patients who underwent mastectomy from December 2019 to September 2021. Patients were contacted by a member of the research team and asked to participate in a COVID-19-specific survey and to complete the Hospital Anxiety and Depression Scale (HADS). Patients were then grouped into "surge" or "nonsurge" groups based on the date of mastectomy. RESULTS: Two hundred and fifty-nine patients were included in this study. During the study period, 42% (n = 111) of the patients underwent breast reconstruction. The "surge" group included 106 patients whereas the "nonsurge" group included 153 patients. Fewer patients began breast reconstruction during the surge period compared with the nonsurge period (34.0% vs. 49.0%, p = 0.017). Eighty-six patients participated in the COVID-19 survey. Forty-one percent (n = 35) of the patients felt that their care was disrupted because of COVID-19. Eighty-three patients completed the HADS survey. Overall, 16.8% and 15.7% of the respondents fell into the moderate to severe ranges for both anxiety and depression scales, respectively. CONCLUSIONS: Patients with breast cancer have faced increased difficulties with access to breast reconstruction throughout the COVID-19 pandemic. Our institution demonstrated decreased rates of breast reconstruction and an increase in anxiety and depression. The positive benefits of breast reconstruction cannot be overlooked when determining resource allocation in the future.

Complications of Lateral Decubitus Positioning During Orthopaedic Surgery.

¼ Lateral decubitus positioning is a nonanatomical position used for multiple orthopaedic procedures to obtain adequate surgical exposure.¼ Unique ophthalmologic, musculoskeletal, neurovascular, and hemodynamic complications may arise inadvertently from positioning.¼ Orthopaedic surgeons should be aware of the possible complications that may manifest from placing patients in the lateral decubitus position to adequately prevent and to properly manage them.

Affi-BAMS™: A Robust Targeted Proteomics Microarray Platform to Measure Histone Post-Translational Modifications.

For targeted protein panels, the ability to specifically assay post-translational modifications (PTMs) in a quantitative, sensitive, and straightforward manner would substantially advance biological and pharmacological studies. The present study highlights the effectiveness of the Affi-BAMS™ epitope-directed affinity bead capture/MALDI MS platform for quantitatively defining complex PTM marks of H3 and H4 histones. Using H3 and H4 histone peptides and isotopically labelled derivatives, this affinity bead and MALDI MS platform achieves a range of >3 orders of magnitude with a technical precision CV of <5%. Using nuclear cellular lysates, Affi-BAMS PTM-peptide capture resolves heterogeneous histone N-terminal PTMs with as little as 100 µg of starting material. In an HDAC inhibitor and MCF7 cell line model, the ability to monitor dynamic histone H3 acetylation and methylation events is further demonstrated (including SILAC quantification). Affi-BAMS (and its capacity for the multiplexing of samples and target PTM-proteins) thus provides a uniquely efficient and effective approach for analyzing dynamic epigenetic histone marks, which is critical for the regulation of chromatin structure and gene expression.

Rating the Rater: A Technique for Minimizing Leniency Bias in Residency Applications.

Each program in the highly competitive match for a surgical residency needs a way to review applicants effectively. Often this task is undertaken by individual faculty members, reviewing an applicant's file and assigning a score. Despite being asked to rate on a standardized scale, our program found that ratings of the same applicants varied dramatically, with certain faculty consistently scoring higher or lower than others. This is termed leniency bias, or the Hawk-Dove effect, and can affect who is invited to interview depending on which faculty are assigned to review an applicant's file. Methods: A technique to minimize leniency bias was developed and applied to this year's 222 applicants for our plastic surgery residency. The effect of the technique was evaluated by comparing variance between ratings of the same applicants by different faculty before and after our technique. Results: The median variance of ratings of the same applicants reduced from 0.68 before correction to 0.18 after correction, demonstrating better agreement between raters of the applicants' scores after our technique had been applied. This year, applying our technique affected whether or not 16 applicants (36% of interviewees) were invited for interview, including one applicant who matched to our program but who otherwise would not have been offered an interview. Conclusions: We present a simple but effective technique to minimize the leniency bias between raters of residency applicants. Our experience with this technique is presented together with instructions and Excel formulae for other programs to use.

Lumbar Disc Herniation: Diagnosis and Management.

Lumbar disc herniations are common causes of lower back pain, neurological dysfunction, and buttock/leg pain. Herniation refers to the displacement of the nucleus pulposus of the intervertebral disc through the annulus fibrosus, thereby causing pressure on the neural elements. The sequalae of lumbar disc herniations range in severity from mild low back and buttock pain to severe cases of inability to ambulate and cauda equina syndrome. Diagnosis is achieved with a thorough history and physical examination along with advanced imaging. Treatment plans are dictated by corresponding patient symptoms and examination findings with their imaging. Most patients can experience relief with nonsurgical measures. However, if symptoms persist or worsen, surgery may be appropriate.

Severe Hypersensitivity Reaction to Dermabond Prineo After Anterior Cervical Discectomy and Fusion: A Case Report.

CASE: A 68-year-old woman underwent an anterior cervical discectomy and fusion for cervical radiculopathy and subsequently developed a severe contact hypersensitivity reaction in response to Dermabond Prineo, beginning 10 days postoperatively. The Dermabond Prineo mesh was removed, and the patient was treated symptomatically with diphenhydramine, systemic steroids, and oral antibiotics, with complete resolution of her symptoms. CONCLUSION: This is the first reported contact hypersensitivity reaction to Dermabond Prineo in the context of spine surgery. Surgeons should be able to recognize this presentation and treat this appropriately.

Static and Dynamic Stabilization of the Shoulder After Total Scapulectomy in an Electric Burn Patient.

ABSTRACT: An otherwise healthy 49-year-old man experienced a high-voltage electrical injury to the left shoulder resulting in total scapulectomy, partial calviculectomy, and a substantial soft tissue defect. The majority of the muscles around his shoulder were debrided because of necrosis, with only the pectoralis and latissimus dorsi muscles remaining attached to the humerus. Surprisingly, the patient's brachial plexus remained intact, and his left elbow, wrist, and hand function were preserved. A novel combination of 3 static and dynamic suspension techniques were used to stabilize his shoulder and prevent traction injury to the brachial plexus. Postoperative follow-up at 1 year demonstrated excellent stability of his reconstructed shoulder, which allowed him to ambulate independently and return to employment.

The Role of Reconstruction on Desmoid Tumor Recurrence: A Systematic Review and Technical Considerations.

BACKGROUND: Desmoid tumors occur throughout the body, presenting as aggressive, locally invasive lesions that can impede quality of life. Many controversies remain regarding the optimal surgical treatment of desmoid. This article presents a systematic review and meta-analysis on surgical management, focusing on risk of recurrence and the utility of reconstruction within this unique patient population. METHODS: A systematic review was conducted to search for articles. The clinical course of patients diagnosed with desmoid tumors and treated by our institution's multidisciplinary team was retrospectively reviewed over a 13-year period. Meta-analysis study findings were compared with our cohort. RESULTS: From the systematic review, 10 studies with level of evidence III were found, which resulted in 981 patients. Twenty patients from our institution met the inclusion criteria for our study. In both our study cohort and the pooled results, recurrence was significantly higher in patients with positive microscopic margin after resection. In our study cohort, patients with recurrence had higher rates of positive margins compared with those without recurrence (83.3% vs 7.1%, P = 0.004), whereas the pooled study showed a difference of margin positivity of 50% vs 40% ( P = 0.01). No patients who underwent reconstruction in our study cohort had a recurrence during the study period. CONCLUSION: In both our cohort and pooled results, recurrence was significantly higher in patients with positive margins after initial resection. Reconstruction was not found to be a risk factor for recurrence. Reconstruction following desmoid tumor resection should be considered a viable option if a large and aggressive resection is required to obtain negative margins.

Chemical Biology Approaches Confirm MCT4 as the Therapeutic Target of a Cellular Optimized Hit.

Lactic acid transport is a key process maintaining glycolytic flux in tumors. Inhibition of this process will result in glycolytic shutdown, impacting on cell growth and survival and thus has been pursued as a therapeutic approach for cancers. Using a cell-based screen in a MCT4-dependent cell line, we identified and optimized compounds for their ability to inhibit the efflux of intracellular lactic acid with good physical and pharmacokinetic properties. To deconvolute the mechanism of lactic acid efflux inhibition, we have developed three assays to measure cellular target engagement. Specifically, we synthesized a biologically active photoaffinity probe (IC50 < 10 nM), and using this probe, we demonstrated selective engagement of MCT4 of our parent molecule through a combination of confocal microscopy and in-cell chemoproteomics. As an orthogonal assay, the cellular thermal shift assay (CETSA) confirmed binding to MCT4 in the cellular system. Comparisons of lactic acid efflux potencies in cells with differential expression of MCT family members further confirmed that the optimized compounds inhibit the efflux of lactic acid through the inhibition of MCT4. Taken together, these data demonstrate the power of orthogonal chemical biology methods to determine cellular target engagement, particularly for proteins not readily amenable to traditional biophysical methods.

An Artificial Intelligence Approach to Predicting Unplanned Intubation Following Anterior Cervical Discectomy and Fusion.

STUDY DESIGN: Level III retrospective database study. OBJECTIVES: The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF). METHODS: The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF. Machine learning analysis was conducted in Python and multivariate regression analysis was conducted in R. C-Statistics area under the curve (AUC) and prediction accuracy were used to measure the classifier's effectiveness in distinguishing cases. RESULTS: In total, 54 502 patients met the study criteria. Of these patients, .51% underwent an unplanned re-intubation. Machine learning algorithms accurately classified between 72%-100% of the test cases with AUC values of between .52-.77. Multivariable regression indicated that the number of levels fused, male sex, COPD, American Society of Anesthesiologists (ASA) > 2, increased operating time, Age > 65, pre-operative weight loss, dialysis, and disseminated cancer were associated with increased risk of unplanned intubation. CONCLUSIONS: The models presented here achieved high accuracy in predicting risk factors for re-intubation following ACDF surgery. Machine learning analysis may be useful in identifying patients who are at a higher risk of unplanned post-operative re-intubation and their treatment plans can be modified to prophylactically prevent respiratory compromise and consequently unplanned re-intubation.

Medical optimization of osteoporosis for adult spinal deformity surgery: a state-of-the-art evidence-based review of current pharmacotherapy.

PURPOSE: Osteoporosis is a common, but challenging phenomenon to overcome in adult spinal deformity (ASD) surgery. Several pharmacological agents are at the surgeon's disposal to optimize the osteoporotic patient prior to undergoing extensive reconstruction. Familiarity with these medications will allow the surgeon to make informed decisions on selecting the most appropriate adjuncts for each individual patient. METHODS: A comprehensive literature review was conducted in PubMed from September 2021 to April 2022. Studies were selected that contained combinations of various terms including osteoporosis, specific medications, spine surgery, fusion, cage subsidence, screw loosening, pull-out, junctional kyphosis/failure. RESULTS: Bisphosphonates, denosumab, selective estrogen receptor modulators, teriparatide, abaloparatide and romosozumab are all pharmacological agents currently available for adjunctive use. While these medications have been shown to have beneficial effects on improving bone mineral density in the osteoporotic patient, varying evidence is available on their specific effects in the context of extensive spine surgery. There is still a lack of human studies with use of the newer agents. CONCLUSION: Bisphosphonates are first-line agents due to their low cost and robust evidence behind their utility. However, in the absence of contraindications, optimizing bone quality with anabolic medications should be strongly considered in preparation for spinal deformity surgeries due to their beneficial and favorable effects on fusion and hardware compared to the anti-resorptive medications.