Prediction model to delirium in hospitalized elderly people
Bibliographic record
Abstract
Introduction: Delirium has a high prevalence in hospitalized elderly patients. This is due to low hospital detection and the absence of a screening instrument. Objective: evaluate predictive variables in the development of delirium in na in-hospital environment. Methods: Cross-sectional study. Data collection was carried out between 2015-2016, with a sample of 493 elderly people. The variables used were age, sex, the reason for hospitalization, Identification of Elderly at Risk (ISAR), delirium during hospitalization using the Confusion Assessment Method, frailty using the Edmonton Scale, the impact of comorbidities by the Charlson Index and hospital immobility. Predictive variables were identified through logistic regression. Results: 469 elderly people were taken. The presence of delirium during hospitalization was mostly observed between 80 and 89 years old (n = 12), female (n = 16), with the most common reasons for hospitalization due to fractures (n = 6) and accident brain vascular (n = 11), 79% chance of surviving in one year using the Charlson Index (n = 11) and with ISAR> 2 (n = 26). There are important associations for the development of delirium for patients who have a 98% chance of surviving in one year (p = 0.05) and with ISAR <2 (p = 0.027), with a 34% increased chance and 38%, respectively. Conclusion: It is observed that, by the results, the predictive variables of inhospital delirium are patients with a 98% chance of survival and with ISAR <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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".