Development of an Algorithm to Screen for Frailty Using the Clinical Frailty Scale with Postoperative Patients Entering Cardiac Rehabilitation
Bibliographic record
Abstract
Purpose: Frailty is not commonly assessed on intake to cardiac rehabilitation (CR), but screening could enable targeted interventions and potentially reduce secondary complications. This study aimed to develop and retrospectively examine the feasibility of utilizing a CR-specific algorithm based on the Clinical Frailty Scale (CFS). Our CFS-CR algorithm endeavoured to screen for frailty in older adults (> 65 y) entering CR following cardiac surgery/procedure. Method: The charts of 30 former patients (mean age: 74.0 ± 6.9 y) were examined by a clinician working in CR. Results: The clinician was unable to score any of the patients based on their medical charts using the CFS-CR due to insufficient data. Documentation was typically limited in the areas of instrumental and basic activities of daily living whereas exercise data were readily available. Conclusions: Current intake documentation in CR limited the ability to retrospectively screen for frailty. This finding suggests a need for a frailty-specific tool to support routine clinical screening. Prospective evaluation of the CFS-CR is warranted to further examine the clinical utility of the algorithm during CR intake assessments.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".