304 Decision-making interventions for pelvic organ prolapse: a systematic review, meta-analysis, and environmental scan
Bibliographic record
Abstract
Introduction People diagnosed with pelvic organ prolapse (POP) face preference-sensitive treatment decisions. We conducted a systematic review and meta-analysis to determine the effect of decision-making interventions for POP on patient-reported outcomes. To gain a more complete understanding of all potentially accessed resources, we also conducted an environmental scan to determine the quantity and quality of online interventions for POP decision-making. Methods We searched databases (inception to August 2022), trial registries, and reference lists. We included studies that compared a decision-making intervention to usual care among patients with POP. We calculated mean difference (MD), 95% confidence intervals (CIs), and statistical heterogeneity (I2) or narratively summarized outcomes. For the environmental scan, we also searched Google, app stores, and clinical society websites. We assessed intervention quality using DISCERN, the IPDAS checklist, and readability metrics. Results We identified seven publications, including 475 patients (mean age, 60 years) across three countries. There were no differences in decisional conflict (MD 0.09, 95% CI -2.91, 3.09, I2=0%), decision regret (MD 0.00, 95%CI -0.22, 0.22, I2=0%), satisfaction (MD -0.10, 95%CI -0.23, 0.03, I2=0%), knowledge, or shared decision-making. Study quality was low to moderate. We included 31 interventions from the environmental scan: 22/31 were not interactive, quality was low (mean DISCERN=48.1/80), and mean reading grade level of 10.2. Discussion Existing interventions for POP did not improve patient-reported outcomes. Interventions were not tested in younger populations. The quality of online interventions is generally low with poor readability. Future research should address these gaps through the user-centered design of digital interventions with younger patients. Conclusions We urgently need to develop high-quality, engaging interventions for POP, especially in younger age groups.
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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 | 0.027 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".