PREDICTING HOSPITALISATIONS: COMPARING EIGHT RISK SCORES IN CARE-DEPENDENT ELDERLY FROM SIX COUNTRIES
Bibliographic record
Abstract
Better prediction of hospitalisations may allow preventive interventions directed to appropriate target groups. We compared the predictive accuracy of eight existing risk scores to hospitalisations among older care dependent home dwelling adults across six countries from the IBenC study. We assessed 2884 persons aged 65 or older, who received professional homecare in six different countries in Europe and followed them for 6 months. Eight existing index risk scores were computed with baseline data: (1) The Changes in Health, End-stage Disease, Signs, and Symptoms Scale (CHESS); (2)Detection of Indicators and Vulnerabilities for Emergency Room Trips (DIVERT); (3); (4) Identification Seniors At Risk Primary Care (ISAR PC); (5) Emergency admission risk likelihood index (EARLI); (6) Sherbrooke Postal Questionnaire (SPQ); (7) the Elders Risk Assessent (ERA), and (8) Community Assessment Risk Screen (CARS). Their accuracy to predict one or more reported hospitalisations or Emergency Department visits was expressed in the area under the ROC curve (AUC). 194 older adults were admitted at the ED and/or hospital ward during the six-month study period. The highest AUC value was found for the EARLI AUC=0.75, followed by DIVERT (AUC =0.69), CHESS (AUC =0.66), CARS (AUC =0.64), ERA (AUC=0.60), ISAR-PC (AUC =0.47) and SPQ (AUC =0.41). Significantly better AUC value were found in persons without a recent admission at baseline for DIVERT,EARLI and CARS risk scores. Our results reveal two promising risk scores: EARLI, and DIVERT. Identification by these risk scores may help to target preventive intervention in high-risk groups.
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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 | 0.003 | 0.009 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".