Evaluation Changes in Indicators of Oncological Service in Corpus Uteri Cancer in Kazakhstan
Bibliographic record
Abstract
Corpus uteri cancer is the sixth most common cancer worldwide. Approximately 417.367 new cases and 97.370 deaths of endometrial cancer occur annually worldwide. Moreover, in many countries such as Europe, the United States, Russia, and Canada, carcinoma of the corpus uteri (endometrial carcinoma) has now surpassed cervical carcinoma as the most common form of malignancy affecting the female genital tract. The aim of the study was to evaluate some indicators of the oncological service in CUC in Kazakhstan in the period from 2009 to 2018.The research material was data from the Ministry of Health of the Republic of Kazakhstan – annual form No. 7 and 35 regarding CUC (ICD 10 – C54) for 2009-2018 – incidence, mortality, early diagnosis, neglect, morphological verification. A retrospective study using descriptive and analytical methods of biomedical statistics was used as the main method.Results. For 2009-2018, 10.522 new cases of CUC were registered in the republic for the first time and 2.774 women died from this disease. The average annual crude incidence rate of CUC was 11.9±0.30/0000 (95% CI=11.3-12.5) and increased in dynamics from 10.7±0.40/0000(2009) to 11.6±0.40/0000 in 2018, the difference was statistically significant (t=1.59 and p=0.112). In dynamics, mortality rates from CUC tended to statistically significant (t=3.89 and p=0.000) decrease from 3.6±0.20/0000 (2009) to 2.5±0.20/0000 in 2018, and the average annual crude mortality rate from corpus uteri cancer was 3.2±0.20/0000 (95% CI=2.9-3.5). The research of the study period reveals a trend: early diagnosis indicators (specific weight of patients with I-II stage) improved from 82.0% (2009) to 88.6% in 2018, and accordingly the specific weight of neglected patients significantly decreased with stage III (from 12.0% to 8.5%) and with stage IV (from 4.7% to 2.8%). The morphological verification indicators for CUC remained virtually unchanged, remaining fairly high 98.6% and 98.8%, respectively, in 2009 and 2018.Conclusions. An analysis of the indicators of the oncological service in CUC revealed an improvement in morphological verification and early diagnosis, a decrease in neglect and mortality rates, which is undoubtedly associated with regular anti-cancer activities in Kazakhstan.Key words: corpus uteri cancer, incidence, mortality, early diagnosis, neglect, morphological verification, Kazakhstan.
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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 | 0.000 |
| 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".