Evaluation of transport-related outcomes for neonatal transport teams with and without physicians
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to evaluate if the presence of a physician in the neonatal transport team (NTT) affects transport-related outcomes and procedural success. DESIGN: Retrospective cohort study with propensity score matching. SETTING: Canadian national study. PATIENTS: Neonatal transports from nontertiary centres between January 2014 and December 2017. INTERVENTIONS: Comparison of transports conducted by NTTs with physicians (MD Group) and without physicians (noMD Group). MAIN OUTCOME MEASURES: The primary outcome was the change in patient acuity as measured by the transport risk index of physiologic severity (TRIPS) score. Secondary outcomes included mortality within 24 hours of NICU admission, clinical complications during transport, procedural success, and stabilization time. RESULTS: Among 9,703 eligible cases, 899 neonatal transports attended by NTTs with physicians were compared to 899 neonatal transports without physicians using propensity score matching. No differences were seen in the improvement of TRIPS score or mortality ≤24 hours of NICU admission. The MD Group had more clinical complications (7.7% versus 5.0%, P=0.02). No differences were seen in success rates of invasive procedures. The MD Group had shorter stabilization times. In multivariable analysis, the MD Group was not a significant predictor for the improvement in TRIPS score after adjustment for covariates. CONCLUSIONS: Neonatal transports conducted by teams including physicians compared to teams without physicians, did not have higher improvement in TRIPS scores and had similar success rates for procedures. These results provide insights for the planning of the structure and training of specialized interfacility neonatal transport programs.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".