Preliminary exploration of the clinical features of Chinese patients with skin malignancies and premalignancies: a retrospective study of 1420 cases from Peking University First Hospital
Bibliographic record
Abstract
BACKGROUND: The epidemiological and clinical characterization data of skin malignancies and premalignancies in Chinese population is scarce and inadequate. OBJECTIVE: To systematically investigate the clinical features and the trend of skin malignancies and premalignancies in 1420 Chinese cases. METHODS: A total of 1398 patients (presenting 1420 skin tumours) were included. Clinical and demographic information for every individual was collected, including age, age of onset, sex, lesion location, disease duration and tumour histology, which was analyzed for each type of skin tumours. RESULTS: The number of skin malignancies and premalignancies increased over time, with Basal cell carcinoma (BCC) as the most common type (30.5%). The majority of the patients were above 60 years of age both at onset and at diagnosis (52.8% and 62.9%, respectively), yet around one-third of patients were between 35-59 years (35.3% and 31.2%, respectively). Skin malignancies and premalignancies were mainly located in the head and neck (58.6%), followed by the trunk (18.3%) and the extremities (15.0%). Of all BCCs, nodular BCC was the most common histologic subtype (62.8%), while 15.8% were classified as aggressive subtypes. Malignant melanoma (MM) comprised the lowest proportion of 3.7%, with 75% located on extremities. The diagnostic accordance rates varied from 49.5% to 90.4%, with BCC being 67.9%. CONCLUSIONS: The clinical features of skin malignancies and premalignancies in this study showed some similarities with those observed in Caucasian and other Asian populations, with several distinguished features in Chinese patients also being recognized. Closer attention to suspicious lesions in young and middle-aged people is needed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".