Characteristics associated with delays in decision to transfer injured patients
Bibliographic record
Abstract
Introduction The regionalized nature of trauma care necessitates interfacility transfer which is vulnerable to delays given its complexity. Little is known about the interval of time a patient spends at the sending hospital prior to when the transfer is initiated—the “decision to transfer” time. This primary objective of the study was to explore the impact of patient, environmental, and institutional characteristics on decision to transfer time. Methods This was a retrospective cohort study of injured adult patients who underwent emergent interfacility transfer by a provincial critical care transport organization over a 31-month period. Quantile regression was used to evaluate the impact of patient, environmental, and institutional characteristics on the time to decision to transfer. Results A total of 1128 patients were included. The median decision to transfer time was 2.42 h and the median total transport time was 3.12 h. The following variables were associated with an increase in time to decision to transfer at the 90th percentile of time: age >75 (+2.47 h), age 66–75 (+3.70 h), age 56–65 (+1.20 h), transfer between 00:00 and 07:59 (+2.08 h), and transfer in the summer (+2.25 h). The following variables were associated with a decrease in time to decision to transfer at the 90 th percentile of time: Glasgow Coma Scale 3–8 (−2.21 h), respiratory rate >30 (−2.01 h), sending site being a community hospital with <100 beds (−4.11 h), or the sending site being a nursing station (−5.66 h). Conclusion Time to decision to transfer was a sizable proportion of the patients interfacility transfer. Older patients were associated with a delay in decision to transfer as were patients transferred overnight and in the summer. These findings may be used to support the implementation of geriatric trauma triage guidelines and promote ongoing education and quality improvement initiatives to decrease delay.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".