Interrater Reliability of the Clinical Frailty Scale by Geriatrician and Intensivist in Patients Admitted to the Intensive Care Unit
Bibliographic record
Abstract
Background The Clinical Frailty Scale (CFS) is a commonly used frailty measure in intensive care unit (ICU) settings. We are interested in the test characteristics, especially interrater reliability, of the CFS in ICU by comparing the scores of intensivists to geriatricians. Methods We conducted a prospective cohort study on a convenience sample of newly admitted patients to an ICU in Edmonton, Canada. An intensivist and a resident in Geriatric Medicine (GM) independently assigned a CFS score on 158 adults within 72 hours of admission. A specialist in Geriatric Medicine assigned a CFS score independently of 20 of the 158 patients to assess agreement between the two raters trained in geriatrics. Predictive validity was captured using mortality and length of stay. Results Agreement on CFS score was fair for intensivists vs. GM resident (kappa 0.32) and for intensivists vs. GM specialist (0.29), but substantial for GM resident vs. staff (0.79). Despite this, the CFS remained prognostically relevant, regardless of rater background. Frailty (CFS ≥ 5) as assessed by either intensivist or GM resident was a strong predictor of in-hospital mortality (odds ratio [OR] 3.6; 95% CI, 1.6-8.4, p = .003 and OR 3.0; 95% CI 1.3-6.9; p = .01, respectively). Frailty was also positively correlated with age, illness severity measured by APACHE II score, and length of hospital stay. Conclusions The interrater reliability of the CFS in ICU settings is fair for intensivists vs. geriatricians.
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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 | 0.016 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".