Clinicopathologic features and new factors affecting constitution of eyelid neoplasms: a 10-year multicentre experience in southern coastal China
Bibliographic record
Abstract
OBJECTIVE: This study aims to delineate the key characteristics of eyelid neoplasms in central and southern coastal China and to explore potential factors affecting tumour development. METHODS: This was a retrospective, multicentre observational study. In total, 1302 cases of eyelid neoplasms diagnosed in 2 tertiary hospitals were reviewed from January 2013 to May 2023. Histogenesis, pathologic diagnosis, and potential risk factors were investigated, and the findings were compared with data from various regions and countries. Age- and sex-specific incidence rates, time trends, distributions across age groups, and the relationship with the Sociodemographic Index, latitude, and altitude were calculated. RESULTS: There were 1162 benign (89.2%) and 140 malignant (10.8%) cases, representing 63 pathologic types. The median age of benign cases (50 years) was significantly lower than that of malignant cases (72 years) (P < 0.05). The most common benign and malignant lesions were intradermal nevus (28.83%) and basal cell carcinoma (BCC) (74.29%). A relatively younger median age (68 years) for sebaceous gland carcinoma (SGC) was observed, with a male preponderance (sex ratio 2:1). Analyses of the proportions of BCC, SGC, and squamous cell carcinoma in eyelid malignancies revealed significant regression equations (P < 0.05). CONCLUSIONS: Most eyelid neoplasms are of epithelial and melanocytic origin, with benign tumours being predominant. The regions with greater Sociodemographic Index exhibit a greater incidence of BCC and a lower incidence of squamous cell carcinoma and SGC. BCC appears to have a greater incidence in regions located at higher latitudes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".