Epidemiological and Pathological Aspects of Head and Neck Cancers in Togo
Bibliographic record
Abstract
Purpose:Head and neck cancers are a major public health issue. Its current incidence is unknown in Togo. This study aimed at determining the epidemiological and histological features of head and neck cancers in Togo. Materials and Methods: We examined data from patients files recorded in the registers of laboratory of pathology of the university teaching hospital of Lomé. The study concerned data of the patients received from January 1994 to December 2013. We selected only the files whose diagnosis was a cancer. The parameters analyzed were: frequency, age, gender of patients, site, macroscopic and histological type of cancer. Results: Epidemiological, we collected 5234 cases of cancer of which 309cases ORL cancers, representing 5.1% of all cancer cases. The annual frequency was 15.09 cases. The average age was 45 years ranging from 3 to 87 years and a peak incidence between 41-50 years (20%). Sex ratio of 1.55. Concerning pathological, the salivary gland cancers were the most prevalent (28.2%) followed by larynx cancers (24%). Four groups histological were observed: Carcinomas 196cases (63.43%), lymphomas 105cases (33.98%), 6 cases sarcomas (1.94%) and 2cases melanomas (0.65%). The squamous carcinoma (40.78%) was the most frequent carcinomas. The high grade non-Hodgkin lymphomas (48,4%) were common with prevalent Burkitt lymphoma. Conclusion: the head and neck cancers are prevalent in young adults in Togo. The squamous carcinoma is the most common histological type.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".