Intubation Competence During Neonatal Fellowship Training
Bibliographic record
Abstract
OBJECTIVES: To characterize neonatal-perinatal medicine fellows' progression toward neonatal intubation procedural competence during fellowship training. METHODS: Multi-center cohort study of neonatal intubation encounters performed by neonatal-perinatal medicine fellows between 2014 through 2018 at North American academic centers in the National Emergency Airway Registry for Neonates. Cumulative sum analysis was used to characterize progression of individual fellows' intubation competence, defined by an 80% overall success rate within 2 intubation attempts. We employed multivariable analysis to assess the independent impact of advancing quarter of fellowship training on intubation success. RESULTS: There were 2297 intubation encounters performed by 92 fellows in 8 hospitals. Of these, 1766 (77%) were successful within 2 attempts. Of the 40 fellows assessed from the start of training, 18 (45%) achieved procedural competence, and 12 (30%) exceeded the deficiency threshold. Among fellows who achieved competence, the number of intubations to meet this threshold was variable, with an absolute range of 8 to 46 procedures. After adjusting for patient and practice characteristics, advancing quarter of training was independently associated with an increased odds of successful intubation (adjusted odds ratio: 1.10; 95% confidence interval 1.07-1.14). CONCLUSIONS: The number of neonatal intubations required to achieve procedural competence is variable, and overall intubation competence rates are modest. Although repetition leads to skill acquisition for many trainees, some learners may require adjunctive educational strategies. An individualized approach to assess trainees' progression toward intubation competence is warranted.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".