Preoperative factors predicting poor outcomes following laparoscopic choledochotomy: a multivariate analysis study
Bibliographic record
Abstract
BACKGROUND: Laparoscopic surgery for common bile duct stones varies procedurally from a transcystic approach to laparoscopic choledochotomy (LC) with or without biliary drainage. However, LC is a difficult procedure with higher documented morbidity than the transcystic approach. We retrospectively investigated risk factors for adverse outcomes of LC. METHODS: We used logistic regression models to assess 4 categories of adverse outcomes: overall, complications, conversion to open operation and failed surgical clearance. We calculated the area under the receiver operating characteristic curve to evaluate diagnostic accuracy. RESULTS: We included 201 patients who underwent LC in our analysis. Adverse outcomes occurred in 48 (23.9%) patients, complications occurred in 43 (21.4%), retained stones were observed in 8 (4%), and conversion to laparotomy occurred in 7 (3.5%). Multivariate analysis showed that total bilirubin (BIL) and the presence of medical risk factors (MRFs) were significant predictors of adverse outcomes and complications. We calculated the probability of adverse outcomes (p) using the following formula: logit(p) = 0.977 (MRFs) + 0.014 (BIL) - 2.919. p = EXP (logit(p)) ÷ [1+EXP (logit(p))]. According to their logit(p), all patients were divided into a low-risk group (logit(p) ≤ -1.32, n = 130) and a high-risk group (logit(p) > -1.32, n = 71). Patients in the low-risk group had about a 1 in 10 chance (12 of 130) of adverse outcomes developing. Of the 71 patients in the high-risk group, 36 (50.7%) experienced adverse outcomes. CONCLUSION: High BIL and the presence of MRFs could predict adverse outcomes in patients undergoing LC.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".