A simple method to predict pretracheal tissue thickness to prevent accidental decannulation in the obese
Bibliographic record
Abstract
OBJECTIVE: Accidental decannulation is the most common and serious complication associated with tracheostomy in obese patients. We lack a simple way to choose appropriate-size tracheostomy tubes in this patient subset. Our purpose was to 1) establish the range of trachea-to-skin soft tissue thickness (TTSSTT) in obese patients and 2) determine which easily obtained anthropometric measurements are most predictive of TTSSTT. STUDY DESIGN: Case series with planned data collection. SETTING: Tertiary care center. SUBJECTS AND METHODS: Forty consenting patients with body mass index ranging from 30 to 70 were evaluated. These patients, from a bariatric clinic, underwent ultrasound (US) of the neck in predetermined sitting, supine, and neck-extended positions (as for tracheostomy). US was performed by a qualified radiologist. Standard anthropometric measurements of weight, height, arm, hip, waist, and neck sizes were performed. Multiple regression analysis was used to determine which anthropometric measurements best correlated with TTSSTT. RESULTS: The TTSSTT, as measured by US in the supine position, ranged from 0.65 to 3.53 cm. Although the anthropometric measurement most predictive of TTSSTT was waist circumference, a combination of the more practical arm and neck circumferences resulted in an equivalent correlation (r = 0.82). The average root mean squared error was 0.4 cm. From the fitted regression equation, a table predicting TTSSTT from neck and arm circumference was prepared. CONCLUSION: TTSSTT can be closely predicted using simple anthropometric tape measures. The predicted TTSSTT can be used to select appropriate tracheostomy tube size in obese patients. Use of this simple tool is expected to significantly reduce the incidence of accidental decannulation in obese patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".