Bibliographic record
Abstract
Contexto: Una de las tareas nucleares que se asignan a los profesores clínicos es capacitar a los estudiantes para escoger entre un grupo de características que presenta un paciente y asignar con precisión una etiqueta diagnóstica con el objetivo final de desarrollar una estrategia de tratamiento apropiada.Durante los últimos 30 años se ha debatido considerablemente en el seno de la literatura en Educación en Ciencias de la Salud, sobre el modelo que mejor describe cómo los clínicos expertos generan decisiones diagnósticas.Propósito: El propósito de este ensayo es proporcionar una revision bibliográfica de la investigación sobre razonamiento clínico.Los puntos fuertes y débiles de las diferentes aproximaciones del razonamiento clínico se examinarán usando una de las principales divergencias se da entre los modelos de estrategia de razonamiento analítico (p.ej., consciente / controlado) versus estrategias de razonamiento no analítico (p.ej.,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".