Age- and Population-Adjusted Trends in Inpatient Surgical Management of Vaginal Prolapse, Rectal Prolapse, and Concurrent Vaginal and Rectal Prolapse Surgery
Bibliographic record
Abstract
ObjectiveTo report age- and population-adjusted trends in the prevalence of inpatient vaginal prolapse (VP), rectal prolapse (RP), and concurrent VP/RP surgical procedures in women in Washington State over a 12-year period.MethodsThe Comprehensive Hospital Abstract Reporting System, an inpatient claims database, was queried for female patients aged 20 years or older with a diagnosis of VP and/or RP and associated surgical procedures from 2008 to 2019. Rates for female patients were adjusted by age and population based on census results.ResultsBetween 2008 and 2019, inpatient admissions for concurrent VP/RP surgery remained stable, with adjusted rates ranging from 1.42 to 3.38 per 100 000, with a majority performed in patients < 80 years old. The population-adjusted rate of inpatient RP repairs remained stable at 3.12 to 5.14 per 100 000. The population-adjusted rate of inpatient VP repairs decreased dramatically, from 81.79 to 6.96 per 100 000.ConclusionsThe rate of inpatient RP and combined RP/VP surgical procedures was low and remained stable, while inpatient VP surgical repairs decreased substantially. Since the dataset is limited to inpatient surgery, this trend may reflect a shift to outpatient settings for VP surgeries. Nationally in the United States, there has been a trend toward multidisciplinary surgical management of concurrent VP/RP. However, this same trend does not appear to be reflected in Washington State, suggesting that nationwide trends may not be reflective of trends within each state. Further study is needed to understand how and why local trends in the management concurrent VP/RP may differ from national trends, and potentially improve concurrent VP/RP management using multidisciplinary approaches.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".