[Factors influencing the postoperative resolution of varicocele-associated scrotal pain].
Bibliographic record
Abstract
OBJECTIVE: To investigate the factors influencing the postoperative resolution of varicocele-associated scrotal pain. METHODS: Using the keywords "varicocele", "testicular pain", "scrotal pain", "painful varicocele", "ligation", and "varicocelectomy", we searched the PubMed, Embase, Cochrane Collaboration's Database, CNKI, Wanfang, and VIP Database up to October 2016 for the studies relating to surgical treatment of varicocele-associated scrotal pain. We assessed the quality of the cohort studies included using the Newcastle-Ottawa Scale and that of the randomized controlled trials included with the Cochrane Collaboration's tool. We conducted a meta-analysis using the RevMan software. RESULTS: Finally 14 studies were included in this meta-analysis, of which, 2 involved the history of disease, 8 involved the nature of pain, 2 involved the intensity of pain, 9 involved the grade of varicocele, 3 involved the side of varicocele, 9 involved surgical approaches, 3 involved surgical techniques, and 4 involved postoperative recurrence. The pain resolution rate was significantly higher after subinguinal ligation than after high or inguinal ligation (RR = 0.82, 95% CI: 0.76－0.89, P <0.01; RR = 0.92, 95% CI: 0.86－0.99, P = 0.02), and so was it after microsurgery than after laparoscopic varicocelectomy (RR = 0.77, 95% CI: 0.60－0.99, P = 0.04). CONCLUSIONS: Subinguinal varicocelectomy and microsurgery are more effective options than laparoscopic and high or trans-inguinal ligation of the spermatic vein for resolution of varicocele-associated scrotal pain, while the history of disease, the nature and intensity of pain, the grade and side of varicocele, or postoperative recurrence cannot be regarded as the influencing factors.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".