Cost-Effectiveness and Choice of Infant Transport Systems
Bibliographic record
Abstract
OBJECTIVE: To compare cost-effectiveness of three types of infant transport models (Emergency Medical Technicians [EMT], Registered Nurses [RN], or Combined Teams [CT] of RNs and Respiratory Therapists) and to derive a decision model to guide choice of a transport system. RESEARCH DESIGN: A prospective, multicenter, observational study was conducted to compare infant physiologic status before and after transport. Cost-effectiveness analysis from the perspective of the third-party payer, sensitivity analysis and threshold analysis were performed. SUBJECTS: All (n = 1931) out born infants with complete transport data admitted to 11 regional tertiary-level Canadian NICUs from January 1996 to October 1997. MEASURES: Change in Transport Risk Index of Physiologic Stability (TRIPS) Score before and after transport, transport costs. RESULTS: Change in TRIPS was predicted by gestational age at transport, transport duration, and pretransport TRIPS score, but not the type (EMT, RN, CT) of transport team, mode (air/ground) or direction (forward/retrograde) of transport, presence of a physician, and other baseline population risks (sex, small for gestational age, antenatal corticosteroid treatment, Apgar score). The RN model is least costly under most assumptions. At high transport volumes (>2760 transports per year) and long average transport times (>6.8 h per transport), the EMT model was less costly. Cost drivers of transport were volume of transport, relative wages of transport personnel, and percent of waiting time dedicated to infant transport. CONCLUSIONS: A deterministic decision-analytic model can be used to model transport cost-effectiveness and derive a threshold analytic chart for identifying the least costly transport model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".