Feasibility of using administrative data for identifying medical reasons to delay hip fracture surgery: a Canadian database study
Bibliographic record
Abstract
PURPOSE: Failure to account for medically necessary delays may lead to an underestimation of early surgery benefits. This study investigated the feasibility of using administrative data to identify the National Institute for Health and Care Excellence (NICE) 124 guideline list of conditions that appropriately delay hip fracture surgery. METHODS: We assembled a list of diagnosis and procedure codes to reflect the NICE 124 conditions. The list was reviewed and updated by an advanced clinical coder. The list was refined by five clinical experts. We then screened Canadian Institute for Health Information discharge abstracts for 153 918 patients surgically treated for a non-pathological first hip fracture between 1 January 2004 and 31 December 2012 for diagnosis codes present on admission and procedure codes that antedated hip fracture surgery. We classified abstracts as having medical reasons for delaying surgery based on the presence of these codes. RESULTS: In total, 10 237 (6.7%; 95% CI 6.5% to 6.8%) patients had diagnostic and procedure codes indicating medical reasons for delay. The most common reasons for medical delay were exacerbation of a chronic chest condition (35.9%) and acute chest infection (23.2%). The proportion of patients with reasons for medical delays increased with time from admission to surgery: 3.9% (95% CI 3.6% to 4.1%) for same day surgery; 4.7% (95% CI 4.5% to 4.8%) for surgery 1 day after admission; 7.1% (95% CI 6.9% to 7.4%) for surgery 2 days after admission; and 15.5% (95% CI 15.1% to 16.0%) for surgery more than 2 days after admission. The trend was seen for admissions on weekday working hours, weekday after hours and on weekends. CONCLUSION: Administrative data can be considered to identify conditions that appropriately delay hip fracture surgery. Accounting for medically necessary delays can improve estimates of the effectiveness of early surgery.
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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 | 0.038 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".