Flexed‐Neck Flexible Nasolaryngoscopy for Evaluation of the Subglottis and Trachea in Children
Bibliographic record
Abstract
OBJECTIVE: Determine if a flexed-neck posture during flexible nasolaryngoscopy (FNL) improves visualization of the subglottis. STUDY DESIGN: Retrospective review of children undergoing FNL in the neutral (FNL) and flexed-neck (FN-FNL) positions. SETTING: Tertiary children's hospital. METHODS: FNL was performed with each child's head in neutral and flexed-neck positions. Videos in each posture were captured and randomized. The most distal view of the subglottis in each position was evaluated with 4 rating scales: (1) subjective view (SV); of the subglottis and trachea, (2) airway grade (AG); most distal anatomical structure visualized, (3) airway area (AA); percentage of the subglottis visualized; and (4) modified Cormack-Lehane grade. RESULTS: Twenty children had 80 FNL views blindly evaluated by 5 pediatric otolaryngologists. The SV, AG, and AA were all significantly better with the neck flexed compared to a neutral position (7.3 vs 3.0, interquartile range [IQR]: 2.0-6.8, P < .001; 2.3 vs 1.5, IQR: 1.0-2.0, P < .001; 3.4 vs 1.7, IQR: 2.3-3.8, P = .001). There was no difference in the modified Cormack-Lehane grade between positions. Interrater reliability was excellent or strong (0.93-0.94, confidence interval: 0.91-0.93). CONCLUSION: FN-FNL is a simple maneuver performed in children undergoing FNL that partially improves the subjective visualization of the subglottis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".