A comparison of the Miller laryngoscope versus the prototype neonatal offset‐blade laryngoscope in a manikin
Bibliographic record
Abstract
Laryngoscope blades used to intubate newborn babies are relatively bulky and frequently exert high pressure on the upper jaw. We tested a prototype neonatal offset-blade laryngoscope (NOBL) developed to overcome these limitations. Our aims were to compare the pressure on the upper jaw exerted by a size 0 Miller laryngoscope and the NOBL on a neonatal manikin, as well as the time taken to intubate the trachea and the area of view of the larynx. Twenty healthcare professionals with more than five years of experience in neonatal intensive care took part; the findings were assessed using pressure-sensitive film and photographs. High-pressure indentation occurred in 17 (85%) attempts using the Miller versus 1 (5%) using the NOBL (p = 0.0001). The median (IQR [range]) pressure exerted with the Miller laryngoscope was 455 (350-526 [75-650]) kPa vs 80 (0-133 [0-195]) kPa with the NOBL (p < 0.0001). The area of pressure exerted with the Miller laryngoscope was 68 (32-82 [0-110]) mm(2) vs 8 (0-23 [0-40]) mm(2) with the NOBL (p < 0.0001). The time to intubate was 8.3 (7.3-10.1[4-19]) s for the Miller and 8.0 (5.6-9.6 [4-13.5]) s for the NOBL (p < 0.0001). The area of view blocked by the Miller laryngoscope was 38% of the oral orifice versus 12% with the NOBL. We conclude that the NOBL significantly reduced undesired pressure on the upper jaw during tracheal intubation and improved the view of the larynx compared with a conventional laryngoscope.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".