Analgesic regimens administered to older adults receiving skilled nursing facility care following hip fracture: a proof-of-concept federated analysis
Bibliographic record
Abstract
BACKGROUND: Although a majority of patients in the U.S. receive post-acute care in skilled nursing facilities (SNFs) following hip fracture, large-sample observational studies of analgesic prescribing and use in SNFs have not been possible due to limitations in available data sources. We conducted a proof-of-concept federated analysis of electronic health records (EHRs) from 11 SNF chains to describe analgesic use during hip fracture post-acute care. METHODS: We included residents with a diagnosis of hip fracture between January 1, 2018 and June 30, 2021 who had at least one administration of an analgesic. Use of analgesics was ascertained from EHR medication orders and medication administration records. We quantified the proportion of residents receiving analgesic regimens based on the medications that were administered up to 100 days after hip fracture diagnosis. Plots visualizing trends in analgesic use were stratified by multiple resident characteristics including age and Alzheimer's Disease and Related Dementias (ADRD) diagnosis. RESULTS: The study included 23,706 residents (mean age 80.5 years, 68.6% female, 87.7% White). Most (~ 60%) residents received opioids + APAP. Monotherapy with APAP or opioids was also common. The most prevalent regimens were oxycodone + APAP (20.1%), hydrocodone + APAP (15.8%), APAP only (15.1%), tramadol + APAP (10.4%), and oxycodone only (4.3%). During the study period, use of APAP-only increased, opioids-only decreased, and opioids + APAP remained stable. Use of APAP-only appeared to be more prevalent among individuals aged > 75 years (versus ≤ 75 years) and those with ADRD (versus without). CONCLUSIONS: We successfully leveraged federated SNF EHR data to describe analgesic use among residents receiving hip fracture post-acute care.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".