Analysis of unsuccessful intubations in neonates using videolaryngoscopy recordings
Bibliographic record
Abstract
OBJECTIVES: Neonatal intubation is a difficult skill to learn and teach. If an attempt is unsuccessful, the intubator and instructor often cannot explain why. This study aims to review videolaryngoscopy recordings of unsuccessful intubations and explain the reasons why attempts were not successful. STUDY DESIGN: This is a descriptive study examining videolaryngoscopy recordings obtained from a randomised controlled trial that evaluated if neonatal intubation success rates of inexperienced trainees were superior if they used a videolaryngoscope compared with a laryngoscope. All recorded unsuccessful intubations were included and reviewed independently by two reviewers blinded to study group. Their assessment was correlated with the intubator's perception as reported in a postintubation questionnaire. The Cormack-Lehane classification system was used for objective assessment of laryngeal view. RESULTS: Recordings and questionnaires from 45 unsuccessful intubations were included (15 intervention and 30 control). The most common reasons for an unsuccessful attempt were oesophageal intubation and failure to recognise the anatomy. In 36 (80%) of intubations, an intubatable view was achieved but was then either lost, not recognised or there was an apparent inability to correctly direct the endotracheal tube. Suctioning was commonly performed but rarely improved the view. CONCLUSIONS: Lack of intubation success was most commonly due to failure to recognise midline anatomical structures. Trainees need to be taught to recognise the uvula and epiglottis and use these landmarks to guide intubation. Excessive secretions are rarely a factor in elective and premedicated intubations, and routine suctioning should be discouraged. Better blade design may make it easier to direct the tube through the vocal cords.
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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 | 0.015 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".