Refractory overactive bladder patients who chose sacral neuromodulation therapy after failed OnabotulinumtoxinA treatment: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To systematically review outcomes in patients with refractory overactive bladder (OAB) patients who underwent sacral neuromodulation therapy (SNM) therapy after unsuccessful onabotulinumtoxinA (BTX) therapy, and to compare outcomes with those who SNM as initial therapy. METHODS: A systematic search of Cochrane Library, Pubmed and Embase databases from July 2002 to November 2019, to analyze randomized controlled trials and retrospective studies of SNM therapy after failed initial BTX therapy. Two reviewers independently screened the studies and extracted data. A quality assessment of the included literature was conducted using Newcastle-Ottawa Scale (NOS), and Stata 12.0 software was used to conduct a meta-analysis of the collected data. RESULTS: A total of seven studies involving 319 patients were finally included. The success rate in refractory OAB patients who used SNM therapy after failed BTX therapy was 58.5%, 95% CI (0.47-0.70). There was no significant difference between refractory OAB patients who chose SNM as replacement therapy after failed BTX therapy and those who used SNM therapy as first choice [RR = 0.96, 95%CI (0.72-1.26), P = 0.735]. CONCLUSION: OAB patients for whom an initial choice of BTX therapy ends in failure or dissatisfaction may consider switching to SNM therapy. There is no difference in outcomes between these patients and those whose first choice was SNM therapy.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".