Can Pediatric Anesthesiologists Detect an Occluded Tracheal Tube in Neonates?
Bibliographic record
Abstract
UNLABELLED: To determine whether pediatric anesthesiologists can reliably detect occluded tracheal tubes, 18 pediatric anesthesiologists who were blindfolded and fitted with earplugs manually ventilated the lungs of 16 neonates. Consent was obtained from the parents of the neonates. All auditory signals from the monitors were silenced. Six conditions were studied (for 3 min each) in random order: three models of Ayre's t-piece with the Jackson Rees modification and two fresh gas flows (FGF) (2 and 6 L/min). During each condition, the tracheal tube was clamped at five predetermined but randomized times. The volume/pressure relationships of the three t-piece models were determined. Tube occlusions were detected more frequently at a low FGF (82%) than at a high FGF (64%) (P < 0.001). Experienced anesthesiologists (>8 yr experience) detected occlusions (83%) more frequently than less experienced (<2 yr experience) anesthesiologists (63%) (P < 0.027). There was no interaction between FGF and experience. The type of circuit did not affect the detection rate. We conclude that during isolated hand ventilation with the t-piece, pediatric anesthesiologists can detect >80% of occluded tubes provided they use a low FGF or have >8 yr experience, but only 60% of occluded tubes at high FGF or if they have <2 yr experience. IMPLICATIONS: Hand ventilation of the lungs in neonates has been used to detect changes in respiratory compliance, but laboratory models have failed to demonstrate its usefulness. We determined that pediatric anesthesiologists could detect 83% of tracheal tube occlusions in neonates if either the fresh gas flow was 2 L/minor the pediatric anesthesiologist was experienced (> 8 yr).
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".