Meta-analysis of the effectiveness and safety of robotic-assisted versus laparoscopic transabdominal preperitoneal repair for inguinal hernia
Bibliographic record
Abstract
BACKGROUND: Inguinal hernia is a common global disease. This study aims to investigate the effectiveness and safety of robot-assisted transabdominal preperitoneal repair (RTAPP) and laparoscopic transabdominal preperitoneal repair (LTAPP) for inguinal hernia. METHODS: We conducted a thorough search in Cochrane Library, Embase, and PubMed for relevant clinical studies. After applying inclusion and exclusion criteria, the quality of selected studies was assessed using the Jadad scale for randomized controlled studies and the Newcastle-Ottawa scale for observational studies. Meta-analysis was performed using RevMan 5.3 software. RESULTS: A total of ten studies were included, comprising two randomized controlled studies and eight non-randomized controlled studies. Meta-analysis results revealed no statistically significant differences between the RTAPP group and the LTAPP group regarding hospital stay [MD = 0.21 days, 95% CI (-0.09, 0.51), P = 0.17], incidence of seroma [OR = 0.85, 95% CI(0.45, 1.59), P = 0.61], overall complication rate [OR = 1.22, 95% CI(0.68, 2.18), P = 0.51], readmission rate [OR = 1.31, 95% CI(0.23, 7.47), P = 0.76], and recurrence rate [OR = 0.82, 95% CI(0.22, 3.07), P = 0.77]. However, the RTAPP group had longer operation time compared to the LTAPP group [MD = 14.02 minutes, 95% CI (6.65, 21.39), P = 0.0002], and the cost of the RTAPP procedure was higher than that of the LTAPP procedure [MD = $4.17 thousand, 95% CI (2.59, 5.76), P<0.00001]. CONCLUSION: RTAPP for inguinal hernia is a safe and feasible approach, however, it is associated with increased operation time and treatment costs.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.066 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".