Early versus Delayed Surgery for Acute Cholecystitis as an Applied Treatment Strategy When Assessed in a Population-Based Cohort
Bibliographic record
Abstract
BACKGROUND: The aims of this study were to describe the surgical management of acute cholecystitis (AC) in a well-defined population-based patient cohort, in particular adherence to and outcome of the early open/laparoscopic cholecystectomy (EC/ELC) strategy. METHODS: The medical records of all patients residing in Stockholm County who were treated for AC during 2003 and 2008 were reviewed according to a standardized protocol. RESULTS: In 2003, 799 patients were admitted 850 times for AC, and the respective figures for 2008 were 833 and 919. The number of patients who underwent EC/ELC increased from 42.9% in 2003 to 47.4% in 2008. In multivariate regression analysis adjusting for age, gender, severity of cholecystitis, maximal CRP and maximal WBC, EC/ELC was associated with shorter operation time but higher perioperative blood loss when compared to delayed open/laparoscopic cholecystectomy (DC/DLC). The odds ratio for completing the procedure laparoscopically was significantly higher in DC/DLC when adjusting for the same covariates. There were no significant differences in peri- or postoperative complications between the groups. CONCLUSION: Strategies should be implemented in order to secure a more evidence-based approach to the surgical treatment of AC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".