Postinsertion Management of Cholecystostomy Tubes for Acute Cholecystitis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Percutaneous gallbladder drainage (PGD) is indicated to treat high-risk patients with acute cholecystitis. Trends suggest increasing use of PGD over time as the population ages and lives longer with multiple comorbidities. There is no consensus on the management of cholecystostomies tube once inserted. This review aims to synthesize and describe the most common protocols in terms of the need and timing of follow-up imaging, management of a destination tube, timing of tube removal, and optimal interval time from tube positioning to delayed cholecystectomy. METHODS: The study protocol has been registered on the International Prospective Register of Systematic Reviews-PROSPERO. Studies on adult patients diagnosed with acute cholecystitis who underwent a PGD from 2000 to November 2023 were included. The databases searched were MEDLINE, Embase, and Cochrane. The quality assessment tools provided by the NHLBI (National Heart, Lung, and Blood Institute) were applied and descriptive statistics were performed. RESULTS: We included 22,349 patients from 94 studies with overall fair quality (6 prospective and 88 retrospective). In 92.7% of papers, the authors checked by imaging all patients with a PGD (41 studies included). Depending on protocol time, 30% of studies performed imaging within the first 2 weeks and 35% before tube removal (40 studies included). In the case of a destination tube, 56% of studies reported removing the tube (25 studies included). In the case of tube removal, the mean time after insertion was more than 4 weeks in 24 of the 33 included studies (73%). Interval cholecystectomies are more frequently performed after 5 weeks from PGD (32/38 included studies, 84%). Limitations included high clinical heterogeneity and prevalent retrospective studies. CONCLUSIONS: A standard management for percutaneous cholecystostomy after insertion is difficult to define based on existing evidence, and currently we can only rely on the most common existing protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".