Pre‐ and posttransfer computed tomography imaging in Canadian trauma centers: A multicenter retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Multiple clinical practice guidelines recommend minimizing radiation in trauma patients but there is a knowledge gap on the importance of this problem for trauma transfers. We aimed to estimate the incidence of pretransfer and repeat posttransfer computed tomography (CT) overall and in patients with an indication for immediate transfer, to assess interhospital practice variation, to identify predictors, and to quantify the influence of pretransfer CT on time to transfer. Methods We conducted a retrospective multicenter cohort study on patients transferred to major trauma centers from 2013 to 2019. Multilevel generalized linear regression was used to generate intraclass correlation coefficients (ICCs) to assess interhospital variation, multilevel logistic regression to generate odds ratios for each predictor, and geometric mean ratios to quantify the influence of CT on time to transfer. Results Of 18,244 patients included, 8501 (47%) had a pretransfer CT and one-quarter (26%) had a repeat posttransfer CT. Interhospital variation was moderate for pretransfer CT (5%-66%, ICC 12.5%) and for repeat posttransfer CT (7%-44%, ICC 14.7%). Pretransfer imaging was more frequent in elders and in males and repeat posttransfer imaging decreased over the study period but was more frequent in patients transferred in from Level III/IV centers than nondesignated hospitals. Time to transfer was doubled in patients who had a pretransfer CT. CONCLUSIONS: Results suggest that pretransfer CT and repeat posttransfer CT are frequent and are subject to significant practice variation. In addition, pretransfer CT is associated with increased times to transfer though additional studies are needed to demonstrate causation. These results highlight potential opportunities to reduce low-value imaging for trauma transfers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".