The impact of delayed source control and antimicrobial therapy in 196 patients with cholecystitis-associated septic shock: a cohort analysis
Bibliographic record
Abstract
Background: Cholecystitis-associated septic shock carries a significant mortality. Our aim was to determine whether timing of source control affects survival in cholecystitis patients with septic shock. Methods: We conducted a nested cohort study of all patients with cholecystitis-associated septic shock from an international, multicentre database (1996–2015). Multivariable logistic regression was performed to determine associations between clinical factors and in-hospital mortality. The results were used to inform a classification and regression tree (CART) analysis that modelled the association between disease severity (APACHE II), time to source control and survival. Results: Among 196 patients with cholecystitis-associated septic shock, overall mortality was 37%. Compared with nonsurvivors (n = 72), survivors (n = 124) had lower mean admission APACHE II scores (21 v. 27, p < 0.001) and lower median admission serum lactate (2.4 v. 6.8 μmol/L, p < 0.001). Survivors were more likely to receive appropriate antimicrobial therapy earlier (median 2.8 v. 6.1 h from shock, p = 0.012). Survivors were also more likely to undergo successful source control earlier (median 9.8 v. 24.7 h from shock, p < 0.001). Adjusting for covariates, APACHE II (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.06–1.21 per increment) and delayed source control > 16 h (OR 4.45, 95% CI 1.88–10.70) were independently associated with increased mortality (all p < 0.001). The CART analysis showed that patients with APACHE II scores of 15–26 benefitted most from source control within 16 h (p < 0.0001). Conclusion: In patients with cholecystitis-associated septic shock, admission APACHE II score and delay in source control (cholecystectomy or percutaneous cholecystostomy drainage) significantly affected hospital outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".