Systematic review and meta-analysis of single-incision versus conventional multiport laparoscopic splenectomy
Bibliographic record
Abstract
BACKGROUND: There is no consensus that single-incision laparoscopic surgery splenectomy (SILS-SP) is on a par with conventional multiport laparoscopic surgery splenectomy (CMLS-SP). AIMS: The aim of this systematic review and meta-analysis was to assess feasibility and safety of SILS-SP when compared with CMLS-SP. MATERIALS AND METHODS: Eligible articles were identified by searching several databases including PubMed, EMBASE, CNKI (China) and the Cochrane Library, up until February 2016. Studies were reviewed independently and rated by Newcastle-Ottawa Quality Assessment Scale. Evaluated outcomes were complications, operative time, post-operative hospital stay, blood loss, starting diet, post-operative pain scores, conversion and analgesic requirements. RESULTS: Ten retrospective studies met the eligibility criteria. Overall, there was no significant difference between SILS-SP and CMLS-SP in complications, operative time, post-operative hospital stay, blood loss, starting diet, post-operative pain scores, conversion and analgesic requirements. CONCLUSIONS: SILS-SP is feasible and safe in certain patients, with no obvious advantages over CMLS-SP. Therefore, it may be considered an alternative to CMLS-SP. We await high-quality, double-blind RCTs. These should include clear statements on standard scores of post-operative pain and cosmetic results, longer follow-up assessment and cost-benefit analysis.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".