The Every Woman Study (EWS) in Kenya: Identifying challenges and opportunities to improve survival and quality of life for women with ovarian cancer
Bibliographic record
Abstract
Background: Ovarian cancer is the second most common cause of death among gynecological cancerpatients in Kenya, and the number of cases is predicted to rise from 1,130 to 2,600 per year by 2040. However, there is very limited diagnostic and treatment capacity. This study aims to establish the first-ever patient experience evidence base of women with ovarian cancer in Kenya and identify challenges and opportunities to improve survival and quality of life.Methods: This was a prospective cross-sectional observational study in which all eligible womenattending Moi Teaching and Referral and Aga Khan Hospitals with a diagnosis of ovarian cancer withinthe previous five years were recruited.Results: Of 104 eligible women with a median age of 51 years, epithelial ovarian cancers were the mostcommon (70%), particularly high-grade serous carcinoma (46%). Most respondents (66%) had advanceddisease. Approximately 24% had to travel for five hours or more to access care. The mean average timefrom experiencing symptoms to diagnosis was 7.4 months. Almost half had never heard of ovariancancer. Approximately 81% reported that their finances had been affected largely by the diagnosis, with55% of these women reporting that their household income had dropped below what they needed tosurvive.Conclusion: The inaccessibility of quality cancer care in Kenya leads to delays in diagnosis andtreatment initiation. Innovative awareness strategies, health provider education, and cost mitigation areneeded to ensure that women can seek help promptly to reduce morbidity and mortality.
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 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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".