TABLE- BASED CLINICAL FRAILTY SCALE—AGREEMENT BETWEEN ED PHYSICIANS, PATIENTS AND CAREGIVERS
Bibliographic record
Abstract
Frailty predicts adverse health outcomes, and the Clinical Frailty Scale (CFS) has been validated internationally to predict adverse outcomes and mortality. Emergency Departments (ED) are challenged to assess frailty due to a lack of training and limited time. We studied the agreement between ED physicians and patient self-assessments using a tablet-based CFS that includes graphics and short descriptors for each of 9 frailty categories. We conducted a prospective observational cohort study of people >65 years seen in the ED of 3 Canadian academic centers. We excluded patients who were critically ill, visually impaired, or unable to communicate in English or French. We compared agreement on the tablet-based CFS between 4 categories of assessors: Patients, ED physicians, trained research assistants and caregivers using the kappa statistic. We enrolled 274/380 eligible patients who provided complete data (72.1%). Their average age was 75.8 years, and 48.9% were female. Their median MOCA score was 23/30 (IQR = 17 – 26) and their median OARS was 26/28 (IQR 22–28). Agreement between physicians and research assistants was good (κ=0.60, 95% CI 0.50 – 0.70), as was physician-caregiver agreement and patient-caregiver agreement (κ=0.66, 95% CI 0.40 – 0.93). Agreement between ED physicians and patients was only moderate (κ =0.47, 95% CI 0.36 – 0.58).ED physicians more often rated patients as frail (40% vs 29%, p<0.001). There was less agreement between ED physicians and patient self-assessments for the CFS compared to physicians-research assistant agreement and care-giver patient assessments. Future research should validate whether patient or physician assessments have higher predictive validity of frailty.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".