USING HOSPITAL ELECTRONIC HEALTH RECORDS TO DETERMINE FRAILTY WITH THE PICTORIAL FIT-FRAIL SCALE
Bibliographic record
Abstract
Abstract Hip fractures are common in older adults, particularly those that are frail. There is no “gold standard” tool to measure frailty retrospectively from hospital Electronic Health Records (EHRs). The aim of this research was to explore the utility of the Pictorial Fit-Frail Scale (PFFS) in determining frailty retrospectively in older adults admitted to hospital with a hip fracture. A random sample of 200 patients who had a hip fracture was extracted from a larger sample of 682 hip fractures that occurred in older adults (65 years and older) admitted to a Level 1 Trauma Center (April 2015-March 2019). Each EHR was reviewed to determine the availability of data and the score for each of the 14 PFFS domains. The majority, 189 (94.5%) of the EHRs, had data to complete 11-14 of the domains. Patient information was often unavailable to complete the domains of daytime tiredness and pain. The average age was 83.2 (SD 8.2), and 73.0% were female. The mean Raw PFFS score prior to admission was 9.7 (SD 6.6), and 45.5% of patients were classified as moderately or severely frail. Using the Standardized PFFS score (adjusted for up to three missing domains), the mean score was 11.8 (SD 7.9), and 58.2% of patients were classified as moderately or severely frail. The hospital EHR can be used to complete the PFFS. This finding provides researchers and healthcare professionals with a way to stratify older hospitalized patients by frailty, which may be a useful tool for evaluating 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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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".