Agreement and predictive value of the Clinical Frailty Scale in hospitalised older patients
Bibliographic record
Abstract
Abstract Purpose: Our objective was to determine the agreement of the Clinical Frailty Scale (CFS) by comparing scores obtained by a senior geriatrician, a junior geriatrician, and by using a classification tree. Additionally, we evaluated the predictive value of the CFS for 6-month mortality after admission to an acute geriatric unit. Methods: This prospective study was conducted in two acute geriatric units in Belgium. The premorbid CFS was determined by senior and junior geriatricians based on clinical judgement. Another junior geriatrician, who did not have a treatment relationship with the patient, scored the CFS using the classification tree. Intraclass correlation coefficient (ICC) was calculated to assess agreement. A ROC curve and Cox regression model determined prognostic value. Results: In total, 97 patients with a mean age of 86 years (SD 5.2) were included. The reliability of the CFS, when determined by the senior geriatrician and the classification tree, was moderate (ICC 0.526, 95% CI [0.366-0.656]). This is similar to the agreement between the senior and junior geriatricians’ CFS (ICC 0.643, 95% CI [0.510-0.746]). The AUC for 6-month mortality based on the senior geriatrician’s CFS was 0.774. Cox regression analysis indicated that severe or very severe frailty was associated with a higher risk of mortality compared to mild or moderate frailty (hazard ratio 3.476, [1.531-7.888], p = 0.003). Conclusion: The CFS classification tree can help standardize CFS scoring, enhancing reliability when used by less experienced raters.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".