Tension‐free vaginal tape: Do patients who fail to follow‐up have the same results as those who do?
Bibliographic record
Abstract
AIMS: To compare success and complication rates of the Tension-free vaginal tape (TVT) between patients with good versus poor follow-up. MATERIALS AND METHODS: A prospective cohort study of 108 women undergoing a TVT procedure was conducted. Patients were seen postoperatively at 6 weeks, 3, 6, 12 months, and yearly thereafter. Patients were categorized as poor follow-up if this schedule was not adhered to. Those who were lost to follow-up at or after their 6-week visit were considered as having failed the procedure. RESULTS: Seventy-nine (73%) patients had good follow-up. Of the remaining 29 patients with poor follow-up, 12 (11%) could not be reached and 17 (16%) were contacted by phone. Reasons given for poor follow-up were: busy or live far from hospital (11), health problems (4), and dissatisfied from surgery (2). Perioperative complication rates were similar between the two groups (P = 0.16). When patients with complete loss to follow-up were analyzed as failures, subjective and objective cure rates were significantly higher in patients with good as opposed to poor follow-up: 92 and 95% versus 72 and 69%, respectively, (P = 0.006). CONCLUSIONS: Patients with poor follow-up probably have lower cure rates after TVT. It is important to follow postoperative patients closely. When reporting success rates, one has to account for all cases to produce realistic results.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".