Predictors of adverse outcomes in elders hospitalised for isolated orthopaedic trauma: a multicentre cohort study
Bibliographic record
Abstract
BACKGROUND: Patients >64 years of age now represent more than 51% of injury hospitalisations in Canada. The tools used to identify older patients who could benefit the most from an interdisciplinary approach include complex parameters difficult to collect in the ED, which suggests that better tools with higher accuracy and using items that can be derived from routinely collected data are needed. We aimed to identify variables that are associated with adverse outcomes in older patients admitted to a trauma centre for an isolated orthopaedic injury. METHODS: We conducted a multicentre retrospective cohort study between 1 April 2013 and 31 March 2019 on older patients hospitalised with a primary diagnosis of isolated orthopaedic injury (n=19 928). Data were extracted from the provincial trauma registry (Registre des traumatismes du Québec). We used multilevel logistic regression to estimate the associations between potential predictors and adverse outcomes (extended length of stay, mortality, complications, unplanned readmission and adverse discharge destination). RESULTS: Increasing age, male sex, specific comorbidities, type of orthopaedic injuries, increasing number of comorbidities, severe orthopaedic injury, head injuries and admission in the year before the injury were all significant predictors of adverse outcomes. CONCLUSION: We identified eight predictors of adverse outcomes in patients >64 years of age admitted to a trauma centre for orthopaedic injury. These variables could eventually be used to develop a clinical decision rule to identify elders who may benefit the most from interdisciplinary 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".