285 DEVELOPING A FRAILTY CARE PATHWAY IN A REHABILITATION HOSPITAL: A PILOT STUDY
Bibliographic record
Abstract
Abstract Background Frailty is a common condition in older adults. The Clinical Frailty Scale (CFS) is a widely used frailty screening-tool within the Irish healthcare system due to its time-efficiency and transferability between settings. The Edmonton Frailty Scale (EFS) is heralded as an effective tool capturing multi-dimensional aspects of frailty. Due to the lack of blanket referral system for some Multi-Disciplinary Team (MDT) members in our Irish rehabilitation hospital, early identification of frailty is key to ensure timely input from all disciplines. To optimise MDT intervention, the EFS was piloted alongside the CFS comparing user-experience and sensitivity. Methods Education sessions were held by frailty-group members to familiarise staff with frailty concepts and frailty-tool administration. The EFS and CFS were administered with all patients over 65 years within 72 hours of admission onto two wards of our hospital over a three-month period. Frailty scale completion was co-ordinated by the physiotherapists and occupational therapists who operate a blanket referral system. Detection of frailty triggered an urgent referral to dietician, medical social work and speech and language therapy colleagues who don’t operate a blanket referral system. The target time for MDT input was two days for the severely frail cohort and one week for mild or moderately frail patients. Results The EFS was administered for 83 patients (mean age: 84 years). Of those, 6% were severely frail, 23% were moderately frail and 28% were mildly frail. The CFS was found to detect a higher frailty level in 47% of patients screened when compared to the EFS and took an average of ten minutes less to administer. Conclusion The CFS will continue to be administered with patients due to its higher sensitivity to frailty and time efficiency for completion. Referrals will continue to be generated to all MDT members to expedite input with frail patients.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".