Correlación de la escala clínica de fragilidad y el algoritmo de fragilidad propuesto por la Universidad Dalhousie en adultos mayores
Bibliographic record
Abstract
Sr. Editor, La fragilidad es considerada un síndrome geriátrico dinámico donde el deterioro cognitivo, el autorreporte de salud y la pérdida de independencia para realizar actividades básicas e instrumentales se van interrelacionando. Por ello, la Universidad de Dalhousie durante la pandemia por COVID-19 propuso elaborar un algoritmo enfocado en las comorbilidades, alteraciones de salud autorreportadas, dependencia para actividades básicas e instrumentales de la vida diaria que podrían ayudar a determinar el nivel de fragilidad en adultos mayores de manera más certera. El presente estudio tuvo como objetivo determinar la relación entre el algoritmo propuesto por la Universidad de Dalhousie y la Escala Clínica de Fragilidad (CFS) en adultos mayores en diferentes niveles asistenciales durante la pandemia por COVID-19 (1,2).
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".