Inter-Rater Reliability of the Retrospectively Assigned Clinical Frailty Scale Score in a Geriatric Outreach Population
Bibliographic record
Abstract
BACKGROUND: Frailty, a common clinical syndrome in older adults associated with increased risk of poor health outcomes, has been retrospectively calculated in previous publications; however, the reliability of retrospectively assigned frailty scores has not been established. The aim of this study was to see if frailty scores, based on chart review data, agreed with clinician-determined scores based on a comprehensive geriatric assessment. METHODS: Per standard practice, all patients seen by one nurse clinician (JW) from the Southwestern Ontario Regional Geriatric Program, a tertiary care-based outreach service, between August 15, 2013 and December 31, 2015 received a comprehensive geriatric assessment which included the assignment of an interview-based Clinical Frailty Scale score (CFS-I). Subsequently, a medical student researcher (JD), blinded to the CFS-I, assigned each consenting patient a frailty score based on chart review data (CFS-C). The inter-rater reliability of the CFS-I and CFS-C was then determined. RESULTS: Of the 41 consented patients, 39 had both a CFS-I and CFSC score. The median CFS score was 6, indicating patients were moderately frail and required assistance for some basic activities of daily living. Cohen's kappa coefficient was 0.64, indicating substantial agreement. CONCLUSION: CFS scores can be reliably assigned retrospectively, thereby strengthening the utility of this measure.
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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 | 0.040 | 0.090 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".