Cancers primitifs oto-rhino-laryngologiques et cervico-maxillo-faciaux: aspects épidémiologiques et histopathologiques
Bibliographic record
Abstract
INTRODUCTION: Establish the panorama of primitive oto-rhino-laryngology and cervico-maxillofacial tumors diagnosed at a reference center in Togo. METHODS: We conducted a retrospective, descriptive study of cancers diagnosed at the ORL and cervico-maxillofacial surgery department of the CHU Sylvanus Olympio of Lomé. It was conducted over a period of 10 years (1 January 2005 - 31 December 2014). RESULTS: ORL and cervico-maxillofacial tumors account for 0.48% of consultations and 15.3% of all ORL tumors. The average age of patients was 47 years, ranging from 3 months to 86 years. We noted a male predominance; the sex ratio was 1.45. Drinking alcohol and smoking tobacco prevailed in the cancer of the larynx. Upper aerodigestive tract (UAT) tumors accounted for 64,8%, with a prevalence of cancers of the oral cavity (36,2% of UAT), followed by cancers of the oropharynx (18,5% of UAT) and finally by cancers of the larynx (18,1% of UAT). Primary malignant cervical adenopathies accounted for 18%. The rarest lesions were cancers of the ear and of maxillomandibular bone tissue (2.24% each). Histology was dominated by squamous cell carcinoma (61.4%) followed by non-Hodgkin lymphoma (23.2%). CONCLUSION: ORL and cervico-maxillofacial tumors are frequent in Togo and can be diagnosed at any age. The predominant tumor types reported are those of the oral cavity, pharynx and primary malignant cervical adenopathies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".