Age and Its Impact on Outcomes with Intra-Abdominal Infection
Bibliographic record
Abstract
BACKGROUND: Age has been shown to play a significant role in the etiology of complicated intra-abdominal infections (cIAIs), but the correlation between age and outcomes after therapy was not investigated in the Study to Optimize Peritoneal Infection Therapy (STOP-IT) trial. PATIENTS AND METHODS: Data were obtained by post hoc analysis of the STOP-IT trial database. Patients were stratified by age <65 or ≥65 years. Primary outcomes were surgical site infection (SSI), recurrent IAI (recIAI), and death. Multivariable analysis was performed to identify independent predictors of outcomes. RESULTS: There were 398 subjects <65 and 120 ≥ 65 years. Overall baseline characteristics of the two groups were similar. The site of infection was similar between groups except: Colon or rectum (48.3% vs. 29.9%, p = 0.0002) and biliary tree (16.7% vs. 9.1%, p = 0.02), which were more common in the older group, whereas small intestine (6.7% vs. 16.3%, p = 0.008) and appendix (4.2% vs.17.1%, p = 0.0004) were more common in the younger group. Among the primary outcomes, only death was significantly different between the age groups and was more prevalent in the ≥65 years group (4 [3.3%] vs. 1 [0.3%], p = 0.01). Surgical site infection (9.2% vs. 7.3%, p = 0.50), recIAI (15.8% vs. 14.4%, p = 0.69), and a composite outcome (26.7% vs. 20.4%, p = 0.14) were statistically similar between the age groups, and this remained true when controlling for other co-variables. Multivariable analyses did not reveal age as an independent predictor of the composite or individual outcomes. CONCLUSION: Patients with a more advanced age demonstrated variable sources of infection relative to the younger cohort, yet received similar treatments. Patient age was not an independent predictor of the undesired cIAI outcomes. These findings suggest that advanced age itself does not play a significant role in predicting these adverse outcomes for cIAIs and does not necessitate an altered treatment tactic.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".