Operationalising a conceptual framework for a contiguous hospitalisation episode to study associations between surgical timing and death after first hip fracture: a Canadian observational study
Bibliographic record
Abstract
OBJECTIVE: We describe steps to operationalise a published conceptual framework for a contiguous hospitalisation episode using acute care hospital discharge abstracts. We then quantified the degree of bias induced by a first abstract episode, which does not account for hospital transfers. DESIGN: Retrospective observational study. SETTING: All acute care hospitals in nine Canadian provinces. PARTICIPANTS: We retrieved acute hospitalisation discharge abstracts for 189 448 patients aged 65 years and older admitted to acute care with hip fracture between 2003 and 2013. PRIMARY AND SECONDARY OUTCOME MEASURES: The percentage of patients treated surgically, delayed to surgery (defined as two or more days after admission) and dying, between contiguous hospitalisation episodes and the first abstract episodes of care. RESULTS: Using contiguous hospitalisation episodes, 91.6% underwent surgery, 35.7% were delayed two or more days after admission and 6.7% died postoperatively, whereas, using the first abstract only, these percentages were 83.7%, 32.5% and 6.5%, respectively. CONCLUSION: We demonstrate that not accounting for hospital transfers when evaluating the association between surgical timing and death underestimates reporting of the percentage of patients treated surgically and delayed to surgery by 9%, and the percentage who die after surgery by 3%. Researchers must be aware of this potential and avoidable bias as, depending on the purpose of the study, erroneous inferences may be drawn.
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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 | 0.113 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".