Epidemiological Study of Cutaneous Malignant Melanoma in Shiraz, Southwest of Iran between 2011 and 2016
Bibliographic record
Abstract
Background: Melanoma is the most deadly of all skin cancers in the world, its incidence rate has increased in the last decades. We aim to define the frequency, and epidemiologic features of Cutaneous Malignant Melanoma Cases diagnosed between 2011 and 2016 in teaching hospitals of Shiraz University of Medical Sciences, southwest of Iran. Materials and Methods: This descriptive cross-sectional study was have performed at hospitals affiliated with Shiraz University of Medical Sciences from 2011 to 2016. Pathology reports collected from the laboratory along with general information such as age, sex, site of the tumor and had ulceration or not. SPSS version 23 statistical software was used for data analyzing. P-values less than 0.05 were considered significant. Results: A total of 183 cases of Cutaneous Malignant Melanoma were registered in Faghihi Teaching hospital, 7 cases in Namazi Teaching Hospital and 6 cases in Motahari clinic in Shiraz, between 2011 and 2016. The average age was 64.5 years with age range of 1year to 89 years, mostly in women (51.5%) and also most of the tumor sites were in the foot 46 (23.5%) and then in the scalp 20 (10.2%). The incidence of ulceration was 41 (20.9%), and the most common stage of the tumor was stage one. Conclusion: The results proved the importance of awareness of the physicians about the frequency and epidemiologic features of Cutaneous Malignant Melanoma in their region that they can diagnose or screen and treat them more earlier in better ways.
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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 | 0.001 |
| 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".