Health care‐associated infections after major cancer surgery
Bibliographic record
Abstract
BACKGROUND: Approximately 1.7 million individuals per year are affected with health care-associated infections (HAIs) in the United States. The authors examined trends in the incidence of HAI after major cancer surgery (MCS) and risk factors for HAI to describe the effects of HAI on mortality after MCS. METHODS: Patients undergoing 1 of 8 MCS procedures within the Nationwide Inpatient Sample between 1999 and 2009 were identified (n = 2,502,686). Generalized linear regression models were used to estimate the impact of the primary predictors (procedure type, age, sex, race, insurance status, Charlson comorbidity index, hospital volume, and hospital bed size) on the odds of HAI and in-hospital mortality. Trends in incidence were evaluated with linear regression. RESULTS: Overall, MCS-associated HAI incidence increased 2.7% per year (P < .001), whereas mortality decreased 1.3% per year (P < .001). Male gender (odds ratio [OR], 1.12, 95% confidence interval [CI], 1.10-1.14), advancing age (OR, 1.02; 95% CI, 1.02-1.02), black race (OR, 1.26; 95% CI, 1.21-1.31), ≥1 comorbidities (OR, from 1.08 [95% CI, 1.04-1.13] to 1.31 [95% CI, 1.27-1.35]), and nonprivate insurance (OR, from 1.18 [95% CI, 1.15-1.22] to 1.67 [95% CI, 1.59-1.76]) were associated with an increased odds of HAI on multivariable analysis. Conversely, increasing hospital volume was associated with lower odds of HAI (OR, 0.999; 95% CI, 0.99-0.99). Patients with MCS-associated HAI had increased odds of mortality (OR, 8.66; 95% CI, 8.51-8.82). CONCLUSIONS: Between 1999 and 2009, the incidence of MCS-associated HAI events increased; however, HAI-associated mortality decreased. That said, significant disparities exist in the hospital and demographic attributes associated with MCS-associated HAI, with attendant health policy implications. Moreover, HAI remains detrimentally linked to mortality during hospitalization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".