Does previous gastrectomy history affect the surgical outcomes of laparoscopic cholecystectomy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This current study aimed to explore whether gastrectomy history influenced surgical outcomes while undergoing laparoscopic cholecystectomy (LC). METHODS: The PubMed, Embase, and Cochrane Library databases were searched for eligible studies from inception to April 29, 2023. The Newcastle-Ottawa Scale (NOS) was adopted to assess the quality of included studies. The mean differences (MDs) and 95% confidence intervals (CIs) were calculated for continuous variables, and the odds ratios (ORs) and 95% CIs were calculated for dichotomous variables. RevMan 5.4 was used for data analysis. RESULTS: Seven studies enrolling 8193 patients were eligible for the final pooling up analysis (380 patients in the previous gastrectomy group and 7813 patients in the non-gastrectomy group). The patients in the gastrectomy group were older (MD = 11.11, 95%CI = 7.80-14.41, P < 0.01) and had a higher portion of males (OR = 3.74, 95%CI = 2.92-4.79, P < 0.01) than patients in the non-gastrectomy group patients. Moreover, the gastrectomy group had longer LC operation time (MD = 34.17, 95%CI = 25.20-43.14, P < 0.01), a higher conversion rate (OR = 6.74, 95%CI = 2.17-20.26, P = 0.01), more intraoperative blood loss (OR = 1.96, 95%CI = 0.59-3.32, P < 0.01) and longer postoperative hospital stays (MD = 1.07, 95%CI = 0.38-1.76, P < 0.01) than the non-gastrectomy group. CONCLUSION: Patients with a previous gastrectomy history had longer operation time, a higher conversion rate, more intraoperative blood loss, and longer postoperative hospital stays than patients without while undergoing LC. Surgeons should pay more attention to these patients and make prudent decisions to avoid worse surgical outcomes as much as possible.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it