Randomized trial of success of pediatric anesthesiologists learning to use two video laryngoscopes
Bibliographic record
Abstract
OBJECTIVES: The primary purpose of this study was to establish the ability of pediatric anesthesiologists to learn to use two video laryngoscopes - the GlideScope(®) system (GS) and the Karl Storz Direct Coupled Interface, DCI(®), (KS). BACKGROUND: The number of intubation attempts required to attain proficiency with a video laryngoscope is not known. METHODS: Baseline intubation times, using direct laryngoscopy, were determined for each anesthesiologist on 20 children. Anesthesiologists were then randomized to perform 20 intubations with the GS or KS before crossing over to the other device. RESULTS: There were 193 successful intubations and eight failed intubations (4.0%) with the GS. Median time-to-intubation with the GS for each anesthesiologist ranged from 24.5 to 32.8 s. There were 193 successful intubations and three failed intubations (1.5%) with the KS (P > 0.05 vs failed attempts with GS). Median time-to-intubation with the KS ranged from 21.9 to 31.1 s. For both the GS and KS, five of eight anesthesiologists met the study definition of 'Success'. There was no correlation between median time-to-intubation with all laryngoscopes combined and years since completion of training. The distribution of Cormack and Lehane scores was almost identical for the GS and KS; there were fewer grade III or IV scores than with direct laryngoscopy (P = 0.03; Fischer's exact test). Mean and median times on intubation no. 16-20 were shorter for the KS than for the GS. CONCLUSIONS: Although only 65% of anesthesiologists attained the stringent study definition of 'Success', all rapidly leaned to use both video laryngoscopes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".