Postsurgery paths and outcomes for hip fracture patients (POST-OP HIP PATHS): a population-based retrospective cohort study protocol
Bibliographic record
Abstract
INTRODUCTION: Hip fracture patients receive varying levels of support posthip fracture surgery and often experience significant disability and increased risk of mortality. Best practice guidelines recommend that all hip fracture patients receive active rehabilitation following their acute care stay, with rehabilitation beginning no later than 6 days following surgery. Nevertheless, patients frequently experience gaps in care including delays and variation in rehabilitation services they receive. We aim to understand the factors that drive these practice variations for older adults following hip fracture surgery, and their impact on patient outcomes. METHODS AND ANALYSIS: We will conduct a retrospective population-based cohort study using routinely collected health administrative data housed at ICES. The study population will include all individuals with a unilateral hip fracture aged 50 and older who underwent surgical repair in Ontario, Canada between 1 January 2015 and 31 December 2018. We will use unadjusted and multilevel, multivariable adjusted regression models to identify predictors of rehabilitation setting, time to rehabilitation and length of rehabilitation, with predictors prespecified including patient sociodemographics, baseline health and characteristics of the acute (surgical) episode. We will examine outcomes after rehabilitation, including place of care/residence at 6 and 12 months postrehabilitation, as well as other short-term and long-term outcomes. ETHICS AND DISSEMINATION: The use of the data in this project is authorised under section 45 of Ontario's Personal Health Information Protection Act and does not require review by a Research Ethics Board. Results will be disseminated through conference presentations and in peer-reviewed journals.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".