Obesity and the Other Independent Predictors in Elective Endotracheal Tube Intubations: A Narrative Review
Bibliographic record
Abstract
Obesity is one of the challenging elements in health care. Studies have shown that as the body mass index (BMI) increases, the risk of chronic conditions tends to increase due to altered physiologic and metabolic demands. In addition to underlying physiological changes, anatomical changes can lead to common procedural challenges, such as difficult intravenous (IV) cannulation, difficult airway, and difficult intubation, which makes their preoperative and postoperative care challenging for the anesthesiologists. According to previous studies, there is no single best predictor for difficult airway or intubations and no designed protocol for choosing an intubation technique in obese patients. Some of the preoperative risk factors and techniques such as the modified Mallampati class, sternomental distance, thyromental distance, neck circumference, indirect mirror laryngoscopy, BMI, and intraoperative risk factors such as inappropriate positioning of the patient, suboptimal medication dosing, inappropriate laryngoscopy device acted as independent predictors for difficult airway and difficult intubation. Analyzing each element's importance and making suitable decisions for the individual will reduce the complications and prepare for unplanned emergencies in the operating room. This review is convincing with previous studies that obesity itself is not an independent predictor. Instead, as a preoperative risk factor, and till date, sternomental distance and the number of intubation attempts were demonstrated as significant independent predictors for adverse events. All the other independent factors and considerations were discussed, which can help with further research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".