Development and internal validation of an equation using anthropometric measures to predict correct endotracheal tube insertion depth
Bibliographic record
Abstract
Purpose To develop, internally validate, and assess the utility of implementing a regression model for determining endotracheal tube (ETT) insertion depth. Research methods We recorded height, weight, age, sex, ETT internal diameter (ID), lip marking, and tracheal position from the electronic record from a random subset of 2,000 intubated subjects obtained from 1 January 2009 to 5 May 2012. A multivariable linear regression model was constructed and validated by a nonparametric bootstrapping technique using unrestricted random sampling methods. A prospective pilot of subjects admitted to the pediatric intensive care unit requiring invasive mechanical ventilatory support was conducted from 7 January 2019 to 31 May 2019. Those with spinal and/or skeletal malformations, without a post-intubation chest-x-ray (CXR) order, or whose CXR quality impaired visualizing the carina and ETT tip, were excluded. The validated regression equation determined insertion depth. CXR following intubation determined ETT position. Demographic data were summarized. Two-tailed, one-sample binomial test of proportions assessed differences in the proportion of correct position by the equation. Main findings Four hundred and seventy-seven subjects included in model construction yielded 10,000 independent samples for internal validation; 55% were female, and the mean age (SD) was 47 (63) months. Bias between bootstrap coefficients and refined model estimates were negligible (P \< 0.01). Eleven subjects in the pilot were female (64%), mean age (SD) of 36.7 (38) months. Four protocol violations (36.4%) resulted in malposition. Subsequent repositioning per protocol resulted in 100% correct positioning (P = 0.01). Conclusion The regression equation \[0.8636 \* (Ht. ^0.6223^)\] facilitated correct ETT placement. A larger, diverse sample is required for external model validation.
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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 | 0.038 | 0.073 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".