Cervical cancer management in a low resource setting: A 10-year review in a tertiary care hospital in Kenya
Bibliographic record
Abstract
Background: Cervical cancer is one of the leading causes of cancer mortality among women in Kenya due to late presentations, poor access to health care, and limited resources. Across many low- and middle-income countries infrastructure and human resources for cervical cancer management are currently insufficient to meet the high population needs therefore patients are not able to get appropriate treatment. Objective: This study aimed to describe the clinicopathological characteristics and the treatment profiles of cervical cancer cases seen at Moi Teaching and Referral Hospital (MTRH). Methods: This was a retrospective cross-sectional study conducted at MTRH involving the review of the electronic database and medical charts of 1541 patients with a histologically confirmed diagnosis of cervical cancer between January 2012 and December 2021. Results: Of the 1541 cases analyzed, 91% were squamous cell carcinomas, 8% were adenocarcinomas, and 1% were other histological types. Thirty-eight percent of the patients were HIV infected and less than 30% of the women had health insurance. A majority (75%) of the patients presented with advanced-stage disease (stage IIB-IV). Only 13.9% received chemoradiotherapy with curative intent; of which 33.8% received suboptimal treatment. Of the 13% who received surgical treatment, 45.3% required adjuvant therapy, of which only 27.5% received treatment. Over 40% of the women were lost to follow-up. Conclusion: Most of the patients with cervical cancer in Kenya present at advanced stages with only a third receiving the necessary treatment while the majority receive only palliative treatment or supportive care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".