The Identification of Seniors At Risk Screening Tool: Further Evidence of Concurrent and Predictive Validity
Bibliographic record
Abstract
OBJECTIVES: To evaluate the validity of the Identification of Seniors at Risk (ISAR) screening tool for detecting severe functional impairment and depression and predicting increased depressive symptoms and increased utilization of health services. SETTING: Four university-affiliated hospitals in Montreal. DESIGN: Data from two previous studies were available: Study 1, in which the ISAR scale was developed (n=1,122), and Study 2, in which it was used to identify patients for a randomized trial of a nursing intervention (n=1,889 with administrative data, of which 520 also had clinical data). PARTICIPANTS: Patients aged 65 and older who were to be released from an emergency department (ED). MEASUREMENTS: Baseline validation criteria included premorbid functional status in both studies and depression in Study 2 only. Increase in depressive symptoms at 4-month follow-up was assessed in Study 2. Information on health services utilization during the 5 months after the ED visit (repeat ED visits and hospitalization in both studies, visits to community health centers in Study 2) was available by linkage with administrative databases. RESULTS: Estimates of the area under the receiver operating characteristic curve (AUC) for concurrent validity of the ISAR scale for severe functional impairment and depression ranged from 0.65 to 0.86. Estimates of the AUC for predictive validity for increased depressive symptoms and high utilization of health services ranged from 0.61 to 0.71. CONCLUSION: The ISAR scale has acceptable to excellent concurrent and predictive validity for a variety of outcomes, including clinical measures and utilization of health services.
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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 | 0.038 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".