Trends in Mode of Gynecologic Surgery for Benign Disease in Brazil
Bibliographic record
Abstract
Objective: There are limited studies on laparoscopy for benign hysterectomy in low- and middle-income countries. This article describes national trends in modes of hysterectomy in Brazil and compares outcomes by regions and health sectors. Materials and Methods: This was a cross sectional retrospective analysis of benign hysterectomies from open-access national databases (public sector: 2008–2017; private sector: 2016) in Brazil. Variables examined included the modes (vaginal, abdominal, and laparoscopic) of hysterectomies, geographic regions, mortality rates, and lengths of stay (LOS). Results: Benign hysterectomies decreased by 19.2% over 10 years. The proportion of abdominal surgeries increased from 85% to 88%. Despite an increase in laparoscopic surgeries (0.2–0.9%), minimally invasive surgery (vaginal and laparoscopic) decreased (14.7%–12.6%), largely driven by a drop in vaginal cases (14.5%–11.7%). More laparoscopic hysterectomies were performed in the private sector compared to the public sector (11% versus 1%; p < 0.001). There were significant geographic disparities, with 17% of hysterectomies in the private sector performed laparoscopically in the south compared to 9% in the northeast (p < 0.001). Conclusions: Trends in modes of hysterectomies have changed. There are regional inequities, with wealthier regions accessing more laparoscopic surgery. Understanding the trends and factors affecting access to laparoscopy is essential for ensuring equitable access to high-quality gynecologic care. (J GYNECOL SURG 37:337)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".