Wounds in Surgical Patients Who Are Obese
Bibliographic record
Abstract
In Brief Surgery, whether bariatric or not, puts this population at risk. Review the basics of prevention and care. Overview The number of surgical patients who are obese in the United States is rising, a trend that's likely to continue. Such patients are at higher risk than nonobese patients are for surgical site infections and other complications such as dehiscence, pressure ulcers, deep tissue injury, and rhabdomyolysis. This article details the factors that can contribute to such complications, including a high number of comorbidities, and offers practical suggestions for preventing them. Nurses should understand that special equipment, precautions, and protocols may be needed at every stage of care, and that obese patients aren't anomalies but rather a part of a growing population with particular needs. Obesity increases the risk of perioperative complications in the skin and underlying tissue, including infection, dehiscence, pressure ulcers, and deep-tissue injury. Vigilant monitoring can be lifesaving.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".