Bibliographic record
Abstract
UNLABELLED: Variation in types and incidence of ocular tumors are frequently seen from one region to another; however in Yemen, publication of eye cancer statistics were not available. This study aims to describe the socio-demographic characteristics and types of eye cancers in Yemen. METHODS: Data were collected from two population cancer registries in Aden and Hadhramout regions (South-Eastern governorates of Yemen) from 1997 to 2008. All cancers related data were analyzed using CanReg4 computer program (IACR, Lyon, France). RESULTS: A total of 92 eye cancer cases were reported with 51 male cases and 41 females and mean age of 40 years (SD±26.6). The calculated annual age-standardized incidence of eye cancers was 1.3 per million male populations and 1.15 per million female populations in the studied areas in Yemen. Around one quarter of cases were reported with squamous cell carcinoma (26%), followed by retinoblastoma (25%). The last was seen dominant among children < 15 years of age (91%) with a mean age of 6.7 years. CONCLUSION: The low proportions of other types of eye cancer in Yemen are probably due to registration of cases with less accurate specification. Thus, under-reporting could be found for those cases living in remote areas where access to specialized health care center is difficult. The given trend of eye cancer will be helpful to provide ophthalmologists and decision makers in the health field with a foundation to monitor future disease patterns in Yemen. Moreover, these data could be utilized for comparison with other selected populations elsewhere.
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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 | 0.001 | 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) | 0.006 | 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".