Validation of patient’s ability to ‘self-frailty score’ using a modified Rockwood frailty score
Bibliographic record
Abstract
Abstract Introduction Frailty scoring plays a vital role in perioperative care, identifying those who benefit from shared decision-making and those at risk of heightened mortality and extended stays. We aimed to assess the feasibility of self-assessing frailty and its potential as a tool for identifying frail patients to enhance perioperative preparation and outcomes. Methods Between August 2024 and January 2025, a modified Rockwood frailty score with eight categories was given to patients in the Older Person’s Assessment Service, Emergency Department, and Outpatient Clinic at Morriston Hospital. Patients completed the score on paper or electronically. Patient and clinician scores were compared and analysed. Results A total of 173 paired questionnaires were completed. Twelve paper questionnaires were excluded due to incomplete responses. No electronically completed questionnaires were excluded. Amongst the remaining 161 paired questionnaires, a strong correlation was observed between patient and clinician, with most discrepancies differing by just one. The highest levels of agreement were in those with mild and moderate frailty, while the most significant discrepancies were in the ‘managing well’ category. The mean self-assessed frailty score was 4.12 (SD = 1.818), and the mean clinician-assessed frailty score was 4.29 (SD = 1.637). The correlation score between self-assessed and clinician-assessed frailty scores was 0.852, which was statistically significant (p < 0.001). Conclusions The strong correlation between patient self-reported and clinician-assessed frailty scores highlights a general agreement between the two perspectives. However, clinicians tend to assign slightly higher frailty levels, particularly in less severe cases. These findings underscore the value of integrating electronic self-screening tools to assess patients for frailty and identify those who would benefit from perioperative assessment.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".