Practical lessons in implementing frailty assessments for hospitalised patients with COPD
Bibliographic record
Abstract
Frailty is a comprehensive health measure characterised by an individual’s vulnerability and diminished reserve when faced with health stressors.1–3 As one becomes increasingly frail, the ability to recover from acute illnesses is impaired, leading to progressive disability, increased risk of hospitalisation, need for supportive living environments and death.1 4–6 Current estimates suggest that 40% of community-dwelling adults are at risk of becoming frail, while 40% of hospitalised patients are ‘vulnerable’ or ‘mildly frail’.5 7 8 Hospitalisation is a key risk factor for the progression of frailty, especially among older adults.5 9–11 Frailty is measured with the Clinical Frailty Scale (CFS), a validated measure that is correlated with a comprehensive Frailty Index.1 Assessing frailty during patient encounters could help clarify the appropriateness of interventions and improve prognostication and shared decision making, which are essential components of patient-centred care.2 12–15 Our health system is currently organised to address single organ illnesses and frailty is often overlooked as a consequence of normal ageing.16 This pilot project was designed to assess the feasibility of implementing the CFS among hospitalised patients with chronic obstructive pulmonary disease (COPD), to assess the differences in frailty assessments between health providers, and to understand the distribution of frailty among hospitalised patients with COPD. Over an 11-month period, the CFS was included in routine nursing assessments on a respiratory ward at a tertiary care hospital (online supplementary appendix 1). Frailty assessments were linked to a clinical pathway designed to allocate supportive resources …
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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 | 0.047 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".