Concordancia interobservador de la clasificación de Eaton-Littler de la artrosis trapeciometacarpiana / Inter-observer agreement on the Eaton-Littler classification of trapeziometacarpal joint osteoarthritis

Objetivo. Estudiar la concordancia de la clasificación radiográfica de Eaton-Littler para el diagnóstico de la artrosis de la articulación trapeciometacarpiana (ATM). La hipótesis nula consideró que la concordancia era debida al azar, y la alternativa que la concordancia no era debida al azar. Material y método. Se estudiaron las radiografías de 46 pacientes con síntomas y signos clínicos de artrosis de la ATM. Se clasificaron independientemente por 5 facultativos: tres expertos en extremidad superior, uno en radiodiagnóstico y un residente en COT de cuarto año. Se estudió la concordancia de las observaciones mediante el índice kappa global. Discusión. El diagnóstico de la artrosis de la ATM se basa fundamentalmente en la clínica, siendo la radiografía un método complementario de confirmación. La clasificación de Eaton-Littler propone 4 estadios evolutivos de la enfermedad, con una implicación terapéutica. Conclusiones. La concordancia de la clasificación radiográfica de Eaton-Littler es moderada (AU)

Objective. To study the concordance of the radiographic classification of Eaton-Littler for the diagnosis of basal thumb joint osteoarthritis (BTJO). The null hypothesis was that the concordance was due to chance and the alternative was that the concordance was not due to chance. Material and method. We have observed the x-rays of 46 patients with symptoms and clinical signs of BTJO. They were independently classified by five physicians: three experts in upper extremity, one in radiology and a fourth year resident of Orthopedics. We studied the concordance in the observations through the global Kappa. Discussion. The diagnosis of BTJO is based primarily on clinical exploration, radiology is a complementary method of confirmation. The classification of Eaton-Littler proposes 4 stages of the disease, with a therapeutic involvement. Conclusions. The concordance of the radiographic classification of Eaton-Littler is moderate (AU)

[Inter-observer agreement on the Eaton-Littler classification of trapeziometacarpal joint osteoarthritis]. / Concordancia interobservador de la clasificación de Eaton-Littler de la artrosis trapeciometacarpiana.

OBJECTIVE: To study the concordance of the radiographic classification of Eaton-Littler for the diagnosis of basal thumb joint osteoarthritis (BTJO). The null hypothesis was that the concordance was due to chance and the alternative was that the concordance was not due to chance. MATERIAL AND METHOD: We have observed the x-rays of 46 patients with symptoms and clinical signs of BTJO. They were independently classified by five physicians: three experts in upper extremity, one in radiology and a fourth year resident of Orthopedics. We studied the concordance in the observations through the global Kappa. DISCUSSION: The diagnosis of BTJO is based primarily on clinical exploration, radiology is a complementary method of confirmation. The classification of Eaton-Littler proposes 4 stages of the disease, with a therapeutic involvement. CONCLUSIONS: The concordance of the radiographic classification of Eaton-Littler is moderate.

Claridad de las recomendaciones al paciente en el informe de alta hospitalaria / Clarity of recommendations for patients in the hospital discharge report

Fundamento. Las recomendaciones al paciente (RP) son una de las partes fundamentales del informe de alta hospitalaria. El objetivo del estudio fue conocer la frecuencia de RP en los informes de distintas especialidades y la proporción de ellas consideradas de fácil comprensión. Material y método. Estudio observacional transversal. Ámbito: hospital general de agudos. Se revisó una muestra de informes de alta con análisis de la frecuencia y tipos de RP según su contenido y claridad. Resultados. Se analizaron 840 informes y 2.097 RP con un promedio de 2,5 RP por informe. Las RP más habituales se referían al seguimiento del paciente (46% del total), seguidas de las recomendaciones específicas para los días inmediatos al alta (37%), solo el 16% se referían a estilos de vida. Los informes de especialidades quirúrgicas contenían 3,2 RP por alta, frente a 2,3 de las especialidades médicas. Un 90,3% de las RP se consideraron de comprensión clara para un paciente medio. Conclusiones. Los informes de alta analizados contenían pocas RP, en especial las referidas a estilos de vida. Una proporción apreciable de las RP no estaban expresadas con suficiente claridad(AU)

Background. Recommendations for patients (RP) are one of the key parts of the hospital discharge report (HDR). The objective was to study the frequency of RP in the HDR of different specialties and the proportion of them considered to be easy to understand. Material and method. Observational and cross-sectional study. Setting. General acute care hospital. Interventions. Review of a sample of HDR, analysis of the frequency and types of PR based on their content and clarity. Results. A total of 840 HDR and 2,097 PR were analysed with an average of 2.5 RP per report. The most common RP referred to the patient follow-up (46% of total), followed by specific recommendations for the days immediately following discharge (37%), with only 16% related to lifestyle. Reports by surgical specialties contained 3.2 RP compared to 2.3 in medical specialties. The large majority (90.3%) of the RP were considered clear to understand for a standard patient. Conclusions. The HDR analysed contained few RP, in particular those related to lifestyles. A substantial proportion of the RP were not expressed with enough clarity(AU)

[Clarity of recommendations for patients in the hospital discharge report]. / Claridad de las recomendaciones al paciente en el informe de alta hospitalaria.

BACKGROUND: Recommendations for patients (RP) are one of the key parts of the hospital discharge report (HDR). The objective was to study the frequency of RP in the HDR of different specialties and the proportion of them considered to be easy to understand. MATERIAL AND METHOD: Observational and cross-sectional study. SETTING: General acute care hospital. INTERVENTIONS: Review of a sample of HDR, analysis of the frequency and types of PR based on their content and clarity. RESULTS: A total of 840 HDR and 2,097 PR were analysed with an average of 2.5 RP per report. The most common RP referred to the patient follow-up (46% of total), followed by specific recommendations for the days immediately following discharge (37%), with only 16% related to lifestyle. Reports by surgical specialties contained 3.2 RP compared to 2.3 in medical specialties. The large majority (90.3%) of the RP were considered clear to understand for a standard patient. CONCLUSIONS: The HDR analysed contained few RP, in particular those related to lifestyles. A substantial proportion of the RP were not expressed with enough clarity.

Survival analysis and Cox regression

The data provided by clinical trials are often expressed in terms of survival. The analysis of survival comprises a series of statistical analytical techniques in which the measurements analysed represent the time elapsed between a given exposure and the outcome of a certain event. Despite the name of these techniques, the outcome in question does not necessarily have to be either survival or death, and may be healing versus no healing, relief versus pain, complication versus no complication, relapse versus no relapse, etc.The present article describes the analysis of survival from both a descriptive perspective, based on the Kaplan–Meier estimation method, and in terms of bivariate comparisons using the log-rank statistic. Likewise, a description is provided of the Cox regression models for the study of risk factors or covariables associated to the probability of survival. These models are defined in both simple and multiple forms, and a description is provided of how they are calculated and how the postulates for application are checked – accompanied by illustrating examples with the shareware application R(AU)

Survival analysis and Cox regression.

The data provided by clinical trials are often expressed in terms of survival. The analysis of survival comprises a series of statistical analytical techniques in which the measurements analysed represent the time elapsed between a given exposure and the outcome of a certain event. Despite the name of these techniques, the outcome in question does not necessarily have to be either survival or death, and may be healing versus no healing, relief versus pain, complication versus no complication, relapse versus no relapse, etc. The present article describes the analysis of survival from both a descriptive perspective, based on the Kaplan-Meier estimation method, and in terms of bivariate comparisons using the log-rank statistic. Likewise, a description is provided of the Cox regression models for the study of risk factors or covariables associated to the probability of survival. These models are defined in both simple and multiple forms, and a description is provided of how they are calculated and how the postulates for application are checked - accompanied by illustrating examples with the shareware application R.

Logistic regression models.

In the health sciences it is quite common to carry out studies designed to determine the influence of one or more variables upon a given response variable. When this response variable is numerical, simple or multiple regression techniques are used, depending on the case. If the response variable is a qualitative variable (dichotomic or polychotomic), as for example the presence or absence of a disease, linear regression methodology is not applicable, and simple or multinomial logistic regression is used, as applicable.

Simple linear and multivariate regression models

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program (AU)

Simple linear and multivariate regression models.

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program.