The Relationship of microbiological examination result profile to the severity of complicated intra-abdominal infection Patients at a tertiary hospital in Indonesia
Bibliographic record
Abstract
Background: Complicated intra-abdominal infection (cIAI) is one of the contributors to non-traumatic morbidity and fatality in clinical settings. Risk stratification severity tools such as the World Society of Emergency Surgery Sepsis Severity Score (WSESSSS) and the Calgary Predisposition, Infection, Response, and Organ Dysfunction (CPIRO) score have been developed to predict clinical outcomes. The association between the causative microorganisms of cIAI and disease severity has not yet been explored, especially using clinical data from Indonesia. Objectives: To determine the relationship between the microbiological examination profiles of species identification and antimicrobial resistance to the severity of patients with cIAI assessed by WSESSSS and CPIRO. Methods: This retrospective observational study analyzed data from the electronic medical record of a tertiary hospital in Indonesia. Patients diagnosed with cIAI who underwent microbiological examination of intraoperative specimens in 2024 were included. Data were analyzed statistically. Results: A total of 102 patients met the inclusion criteria. The mean WSESSSS was 7.59 ± 3.0 (median 8.0; IQR 4.0). The mean CPIRO score was 1.65 ± 1.27 (median 1.0; IQR 1.0). Escherichia coli was the most frequently found microorganism (53.59%). ESBL-producing bacteria were found in 49 isolates (32.03%) and carbapenem-resistant bacteria in 11 isolates (7.19%). The WSESSSS were significantly higher in the patient with resistant bacteria group compared to the non-resistant group (p<0.001; r=0.37). Conclusion: A significant relationship was found between the microorganism resistance profiles and disease severity in patients with cIAI as assessed by the WSESSSS, while the CPIRO was not significantly associated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".