Wound Infection Rates in Elective Plastic Surgery for HIV-Positive Patients
Bibliographic record
Abstract
BACKGROUND: Human immunodeficiency virus (HIV)-positive patients suffer from a unique set of aesthetic challenges, frequently requiring plastic and reconstructive surgical intervention. This study was designed to evaluate the overall wound infection rates for elective surgery in this patient population, focusing specifically on differences between transdermal (both open and minimally invasive) and transoral procedures. METHODS: Charts were reviewed for all patients with an International Classification of Diseases, Ninth Edition code of V08 (asymptomatic HIV infection, CD4 count >200 cells/microl) who underwent surgery by the senior author (S.P.D.) at this tertiary care hospital between January 1, 2000, and October 1, 2007 (39 patients, 98 procedures). Indication for surgery, type of procedure performed, wound infection rates, length of follow-up, status of HIV infection, and HIV treatment status were all documented. Data were collected according to internal review board protocol. Infection rates were compared between study groups and with the existing surgical literature. RESULTS: Statistical analysis revealed no significant difference in wound infection rate between open and minimally invasive procedures when a transdermal approach is used (10 percent and 0 percent, respectively; p > 0.05). However, there was a significantly increased infection rate in transoral surgery when compared with these two groups (71 percent; p < 0.001). CONCLUSIONS: These findings indicate that there is a greatly increased risk of wound infections for HIV-positive patients undergoing transoral surgery when compared with transdermal surgery and historical norms.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".