Abstracts from the 44th Annual Scientific Meeting of the Canadian Geriatrics Society
Bibliographic record
Abstract
Background/Purpose: Frailty in older adults can increase the risk of adverse health outcomes after surgery.This retrospective study investigated how the change in frailty scores following hip fracture relates to health outcomes. Method:The Pictorial Fit-Frail Scale (PFFS) scores were determined retrospectively by review of hospital electronic health records (EHRs) pre-admission and five days post-surgery on a random sample of 181 hip fracture patients 65 years and older.The change in PFFS scores were categorized as: no change (0 to <4), mild (4 to <8), moderate (8 to <12), and severe change (12 or greater).Associations between frailty change categories and length of stay (LOS), mortality, discharge location, and alternate level of care (ALC) days in acute care were analyzed using partial correlations. Results:The average age was 83.1 years (SD 8.2) and 74.0%female.The mean PFFS scores were 10.9 (SD 7.4) prior to admission and 19.2 (SD 5.5) five days post-surgery.Almost a quarter (23.2%) had a severe change in PFFS score and 39.2% had a mild or no change.There was a statistically significant positive correlation between the magnitude of change of the PFFS score and the LOS (r=0.21),ALC days (r=0.23),mortality (r=0.22), and not returning home (r=0.46)when controlling for age and PFFS pre-admission.Discussion: Health data collected in hospital EHRs can be used to determine frailty levels pre-admission and post-surgery.The change in frailty, measured by the PFFS, correlates with health outcomes.Patients with severe changes in frailty post-surgery have worse health outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.163 | 0.064 |
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".