Impact of Ambulance Transportation on Resource Use in the Emergency Department
Bibliographic record
Abstract
OBJECTIVE: To determine how ambulance transportation is associated with resource use in the emergency department (ED). METHODS: A retrospective administrative database review of patient visits to a Montreal tertiary care hospital ED in one year (April 2000-March 2001). Measures of resource use included ED length of stay, admission to the hospital, and whether consultations and radiology/imaging tests (excluding plain-film x-rays) were ordered from the ED. RESULTS: During the study period, 39,674 patients made 59,142 visits to the ED. Ambulance transportation was used for 15.6% of these ED visits. Compared with non-ambulance visits, ambulance visits were more likely to be made by older patients (mean age: 68 vs. 47 years), to be made by females (59% vs. 55%), to have a greater triage urgency score (mean on 1-5 scale, with 1 most urgent: 2.7 vs. 3.9), and to occur after office hours, 5 PM to 9 AM (47% vs. 43%). Ambulance visits were also more likely than non-ambulance visits to result in: a longer length of stay (mean: 13.3 hours [95% CI = 13.0 to 13.6] vs. 5.9 [95% CI = 5.8 to 6.0]), hospital admission (40% vs. 10%) (odds ratio [OR]: 5.94 [95% CI = 5.59 to 6.33]), consultations (56% vs. 20%) (OR: 5.15 [95% = 4.86 to 5.45]), and radiology/imaging tests (20% vs. 12%) (OR: 1.93 [95% CI = 1.81 to 2.07]). In multivariate models that adjusted for the effects of age, gender, triage urgency, and temporal factors, ambulance transportation maintained its association with greater resource use. CONCLUSIONS: This preliminary study indicates that patients arriving at the ED by ambulance use significantly more resources than their walk-in counterparts.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".