14P Clinical outcomes of women who attend the Cameroon Baptist Convention Health Services (CBCHS) with cervical cancer
Bibliographic record
Abstract
Cervical cancer ranks the fourth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths among women globally. In LMIC, most women with cervical cancer are diagnosed at an advanced stage because they have limited access to proper diagnosis. Treatment options are limited due to limited access to radiation therapy. Thus, survival outcomes are poor. There is no data on this issue in Cameroon so we undertook to determine the survival outcomes for women who present with cervical cancer to the CBCHS. Data was extracted Women’s Health Program (WHP) database. Outcomes were categorized as alive with disease, alive without disease, or dead. Kaplan-Meier (KM) curves for survival were plotted stratified by age, HIV status, and histologic subtype. Cox regression model for survival analysis was used to determine the impact of some variables on the mean time of patient survival after diagnosis. Between 2013 and 2018, 752 women were diagnosed with cervical cancer. The average age at cervical cancer diagnosis was 53.33 (+/-13.82) with a mean survival time of 2.34 years (+/-2.00). Within five years of diagnosis, the overall survival for women diagnosed with cervical cancer was 27.1%. 285 (37.5%) of cases diagnosed did not go in for treatment. 387 (51.5%) went in for treatment, including 205 who did not complete their treatment. Age at diagnosis (HR 1.007 (95% Cl (1.000-1.013)), p=0.035), a positive HIV status (HR 1.032 (95% Cl (0.930-1.145)), p = 0.558), and histologic subtype of adenocarcinoma (HR 1.026 (95% Cl (0.705-1.493)), p=0.894) were associated with lower survival (although these associations were not statistically significant). A diagnosis of cervical cancer is a serious threat to the health of women, especially in LMIC like Cameroon. Survival from the disease is extremely poor in this country, consistent with data from other LMICs. Most cases present late with symptoms, and the majority cannot afford treatment reflected by the very few who attend recommended forms of treatment or are unable to complete it. Education, and creating awareness around primary and secondary prevention and universal health care funding are necessary steps to strengthen cervical cancer control in Cameroon.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".