Modelo predictor de ingreso hospitalario a la llegada al servicio de urgencias.
Bibliographic record
Abstract
Objetivo: desarrollar un modelo de predicción de ingreso hospitalario a la llegada del paciente a urgencias, con el fin de conocer la necesidad de camas hospitalarias casi a tiempo real, y así preveer los recursos asistenciales necesarios de forma precoz. Material y métodos: estudio observacional de cohorte prospectivo. Se incluyeron todos los pacientes consecutivos filiados para el triaje entre las 8-22 horas de un servicio de urgencias de un hospital terciario durante un mes. Se analizaron 7 variables a la llegada del paciente a urgencias que pudieran influir en el ingreso: edad, sexo, nivel de gravedad según el triaje, ubicación inicial, diagnóstico de entrada, solicitud de prueba complementaria y prescripción de medicación. Se realizó un estudio multivariable según regresión logística. Resultados: se incluyeron 2476 episodios de los que 114 (4,6%) ingresaron. Se asociaron de forma significativa: edad >65 años (odds ratio[OR]=2,1, intervalo de confianza [IC] 95%,1,3-3,2; p=0,001); sexo masculino (OR=1,6, IC95%,1,1-2,4; p=0,020); diagnóstico de entrada disnea (OR=5,2, IC95%, 2,8-9,7; p
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".