Myer‐Cotton Grade of Subglottic Stenosis Depends on Style of Endotracheal Tube Used
Bibliographic record
Abstract
Objectives Determine percentage of subglottic stenosis using current endotracheal tube (ETT) cross‐sectional areas as actual, compared with previously published ETT cross‐sectional areas as expected, and determine if style of ETT could result in a change in percentage of stenosis or Myer‐Cotton grade. Study Type Cross‐sectional study. Design Prospective analysis. Methods Eight styles of uncuffed pediatric ETT from four manufacturers ranging from 2.0 to 6.0 inner diameter (ID) were evaluated. ID and outer diameter (OD) measurements were obtained from each company's specification sheets. Cross‐sectional area was calculated for each ETT using the formula (Area = πr2). The cross‐sectional areas of each current ETT (actual) were compared with those of previously published ETTs (expected) based on age, and the degree of stenosis was calculated using the formula [1‐ (Area actual/Area expected)] × 100%. Ranges of percentage for each style of ETT were calculated. Results There was an increase in range of OD and area with increasing size of ETT ID, with the largest range in OD being 0.8 mm, and the largest range in area being 10.55 mm2. The median interquartile range (IQR), range of percentage stenoses was 11 (5%), ranging from 0% to 21%. Seven of 28 (25%) ranges were found to span two Myer‐Cotton grades. Conclusions The Myer‐Cotton grade of subglottic stenosis depends on the style of ETT used. Using updated values from currently available ETTs aims to keep this grading system valid with respect to surgical approach and outcomes following surgery. Level of Evidence NA Laryngoscope, 133:2808–2812, 2023
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".