The Prevalence, Indications, Outcomes of the Most Common Major Gynecological Surgeries in Kazakhstan and Recommendations for Potential Improvements into Public Health and Clinical Practice: Analysis of the National Electronic Healthcare System (2014–2019)
Bibliographic record
Abstract
OBJECTIVES: Major gynecological surgeries are indicated for the treatment of female genital pathologies. It is key to examine trends in gynecologic surgical procedures and updated recommendations by international gynecological societies to find opportunities for improvement of local guidelines. To date, a very limited number of reports have been published on the epidemiology of gynecological surgeries in Kazakhstan. Moreover, some local guidelines for gynecological conditions do not comply with the international recommendations. Thus, this study aims to investigate the prevalence, indications, and outcomes of the most common major gynecological surgeries by analyzing large-scale Kazakhstani healthcare data, and identifying possible opportunities for improvement of the local public health and clinical practice. METHODS: A descriptive, population-based study among women who underwent a gynecological surgery in healthcare settings across the Republic of Kazakhstan during the period of 2014-2019 was performed. Data were collected from the Unified Nationwide Electronic Health System (UNEHS). RESULTS: In total, 80,401 surgery cases were identified and analyzed in the UNEHS database for a period of 6 years (2014-2019). The median age of the participants was 40 years old, with 61.1% in reproductive age. The most prevalent intervention was a unilateral salpingectomy-29.4%, with 72.6% patients aged between 18-34 years. The proportion of different types of hysterectomies was 49.4%. In 20% of cases, subtotal abdominal hysterectomy was performed due to uterine leiomyoma. The proportion of laparoscopic procedures in Kazakhstani gynecological practice is as low-11.59%. CONCLUSIONS: The Kazakhstani public health and gynecological care sector should reinforce implementation of contemporary treatment methods and up-to-date policies and guidelines. The overall trends in surgical procedures performed for gynecological pathologies, including uterine leiomyoma and ectopic pregnancy treatment, should be changed in favor of the minimally invasive methods in order to adopt a fertility-sparing approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".