COVID-19: Critical appraisal of the evidence
Bibliographic record
Abstract
We present the summary of a critical appraisal document of the available evidence on COVID-19, developed with a clinical practice guide format following GRADE methodology. The document tries to provide answers to a series of structured clinical questions, with an explicit definition of the population, intervention / exposure, comparison and outcome, and a rating of the clinical relevance of the outcome measures. We conducted a systematic review of the literature to answer the questions, grouped into six chapters: epidemiology, clinical practice, diagnosis, treatment, prevention, and vaccination. We assessed the risk of bias of the selected studies with standard instruments (RoB-2, ROBINS-I, QUADAS and Newcastle-Ottawa). We constructed evidence tables and, when necessary and possible, meta-analysis of the of the most relevant outcome measures. We followed the GRADE system to synthesise the evidence, assessing its quality, and, when appropriate, giving recommendations, rated according to the quality of the evidence, the values and preferences, the balance between benefits, risks and costs, equity and feasibility. Presentamos el resumen de un documento de valoración crítica de la evidencia disponible sobre COVID-19, elaborado con formato de guía de práctica clínica siguiendo metodología GRADE. El documento trata de dar respuestas a una serie de preguntas clínicas estructuradas, con definición explícita de la población, intervención/exposición, comparación y resultado, y una jerarquización de la importancia clínica de las medidas de efecto valoradas. Realizamos revisiones sistemáticas de la literatura para responder a las preguntas, agrupadas en seis capítulos: epidemiología, clínica, diagnóstico, tratamiento, prevención y vacunas. Valoramos el riesgo de sesgo de los estudios seleccionados con instrumentos estándar (RoB-2, ROBINS-I, QUADAS y Newcastle-Ottawa). Elaboramos tablas de evidencia y, cuando fue necesario y posible, metanálisis de las principales medidas de efecto. Seguimos el sistema GRADE para realizar síntesis de la evidencia, con valoración de su calidad, y, cuando se consideró apropiado, emitir recomendaciones, jerarquizadas en función de la calidad de la evidencia, los valores y preferencias, el balance entre beneficios, riesgos y costes, la equidad y la factibilidad.
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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 | 0.363 | 0.693 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.043 | 0.027 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.042 | 0.015 |
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".