Predicting the Success of Endoscopic Transpapillary Gallbladder Drainage for Patients with Acute Cholecystitis During Pretreatment Evaluation
Bibliographic record
Abstract
INTRODUCTION: Although endoscopic transpapillary gallbladder drainage (ETGBD) has been reported to be an effective treatment for acute cholecystitis, technical difficulties have precluded more widespread use of this technique. Case evaluations that can predict the occurrence of such difficulties should increase the acceptance of ETGBD for acute cholecystitis treatment. OBJECTIVE: To establish a pretreatment evaluation protocol for patients with acute cholecystitis. METHODS: Eleven patients with acute cholecystitis who received ETGBD in 2003 or 2004 were enrolled in the present retrospective study. The frequency of success, complications and overall effectiveness of ETGBD for treatment of cholecystitis were measured. Factors that could affect ETGBD success, including clinical and laboratory parameters, and gallbladder ultrasonograms, were also evaluated. RESULTS: ETGBD was successful in seven of 11 patients (success rate 63.6%). All seven patients who underwent ETGBD successfully were afebrile and asymptomatic within a few days. No clinical or laboratory variables were significantly associated with the success of ETGBD. In contrast, ultrasonographic measures of gallbladder minor-axis length and wall thickness in successful cases were significantly shorter (27.4 mm versus 38.0 mm; P=0.008) and thinner (4.2 mm versus 9.0 mm; P=0.041) relative to unsuccessful cases. CONCLUSIONS: Ultrasonographic measures of gallbladder minor-axis length and wall thickness can serve as important predictors of ETGBD technical difficulties during pretreatment evaluation of patients with acute cholecystitis.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".