Reporting interhospital neonatal intensive care transport: international five-step Delphi-based template
Bibliographic record
Abstract
OBJECTIVE: To develop a general and internationally applicable template of data variables for reporting interhospital neonatal intensive care transports. DESIGN: A five-step Delphi method. SETTING: A group of experts was guided through a formal consensus process using email. SUBJECTS: 12 experts in neonatal intensive care transports from Canada, Denmark, Norway, the UK and the USA. Four women and eight men. The experts were neonatologists, anaesthesiologists, intensive care nurse, anaesthetic nurse, medical leaders, researchers and a parent representative. MAIN OUTCOME MEASURES: 37 data variables were included in the final template. RESULTS: Consensus was achieved on a template of 37 data variables with definitions. 30 variables to be registered for each transport and 7 for annual registration of the system of the transport service. 11 data variables under the category structure, 20 under process and 6 under outcome. CONCLUSIONS: We developed a template with a set of data variables to be registered for neonatal intensive care transports. To register the same data will enable larger datasets and comparing services.
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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 | 0.212 | 0.244 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".