Analysis of instruments for the determination of psychiatric comorbidity in adult patients with head trauma
Bibliographic record
Abstract
En este trabajo de grado titulado “Análisis de instrumentos clínicos para la determinación temprana de comorbilidad psiquiátrica en pacientes adultos con trauma craneoencefálico”, se pretendió identificar, analizar y comparar los diferentes instrumentos, escalas y herramientas disponibles mundialmente, para determinar comorbilidad psiquiátrica posterior a un trauma craneoencefálico. Posteriormente, entregar recomendaciones sobre cuáles de los instrumentos son adecuados para el uso del médico no psiquiatra. La metodología usada fue realizar la búsqueda de literatura en las bases de datos PubMed y ScienceDirect. Se encontraron 124 instrumentos de los cuales 60 estaban validados al español, por lo tanto estos fueron usados para el análisis y comparación entre los mismos. Se concluyó que los instrumentos considerados de mayor utilidad, más completos, fáciles de usar y con los mejores índices psicométricos son: para la detección y orientación diagnóstica psiquiátrica Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I), para la evaluación de calidad de vida el Quality of Life after Brain Injury QOLIBRI, para evaluar nivel de funcionamiento el Functional Independence Measure FIM, para evaluar los trastornos afectivos son Beck Depression Inventory II, para la evaluación de trastornos de ansiedad y TEPT la Hospital Anxiety and Depression Scale HADS y la Escala Posttraumatic Stress Disorder Checklist - Civilian version, para la evaluación de trastornos cognitivos Montreal Cognitive Assessment MoCA, para trastornos debidos al consumo de alcohol y SPA World Health Organization’s Alcohol Use Disorders Identification Test AUDIT, y finalmente para trastornos del sueño Pittsburgh Sleep Quality Index PSQI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".