Epidemiological and Pathological Aspects of Cervical Cancer in Ivory Coast
Bibliographic record
Abstract
Cervical cancer is the most common and the leading cause of women death in developing countries. Purpose: To specify the epidemiological and pathological characteristics of cervical cancers in Ivory Coast. Material and methods: This was a retrospective and descriptive study on the cervical cancers histologically confirmed and identified from the registers for recording laboratory of pathological anatomy of Abidjan teaching hospital. The study period was 24 years (January 1984 to December 2007). The parameters analyzed were: frequency, age, socio-demographic status, macroscopic and histological aspects and the prognosis. Results: The cervical cancer represented 78.78% (2064 cases) of gynecological cancers, 42.71% of woman cancer and 17.41% of all cancers. The average age was 48.36 years ranging from 2 to 88 years and a peak incidence between 45-54 years (29%). Multiparity was observed 57.04% (n = 231) and the low socioeconomic level was predominant (70.41%). Concerning pathological examination, the tumor lesions were predominantly budding (51.52%). Squamous cell carcinomas (92.88%) were the most frequent of histological types with 95.1% (n = 1823) of invasive carcinomas. The average age of patients with squamous cell carcinoma was 49 years with 41.5 years for intraepithelial carcinomas and 46.8 years for invasive carcinomas. At the prognosis, squamous cell carcinomas were diagnosed most often in stage pT2 (57.41%) and with extra-cervical represented 66.4% (n = 519). Conclusion: Cervical cancer is the most common cancer in Ivory Coast taking into account male and female together. Its poor prognosis associated with late diagnosis should encourage the establishment of a cytology screening program.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".