Comparative Analysis of Acral Melanoma in Chinese and Caucasian Patients
Bibliographic record
Abstract
Abstract Background Acral melanoma is the most common subtype of melanoma in Chinese patients and one of the least common in Caucasian patients. It has been unclear if outcomes differ between Chinese and Caucasian patients diagnosed with Acral Melanoma. This study investigated patient characteristics and survival differences between Chinese and Caucasian Acral Melanoma patients. Methods Two large institutional melanoma databases from Fudan University Shanghai Cancer Center (FUSCC) and Mayo Clinic enterprise, were retrospectively reviewed from 2009 to 2015. Clinicopathologic and survival data were collected and analyzed between the two groups. The primary outcome was disease-specific survival (DSS) and was calculated using the Kaplan Meier (KM) method. Results The Chinese group presented with more advanced disease compared with Caucasians: thicker Breslow depth (median 3.0 mm vs. 1.2 mm, p=0.003), more ulcerated disease (66.1% vs 29%; p<0.001), and advanced stages (stage II/III 84.3% vs. 37.1%; p<0.001). No significant difference was identified in terms of age at diagnosis, location, histologic subtypes, or node positive rate. The 5-year DSS rate was 68.4% and 73% (p=0.56) for Chinese and Caucasians, respectively. Male gender, Breslow thickness, ulceration, and positive sentinel lymph nodes predicted worse DSS on multivariate Cox regression analysis. Conclusions There appears to be no difference in stage-stratified survival between Chinese and Caucasians, supporting the implementation of clinical trials of AM that could include both Chinese and Caucasian patients.
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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 | 0.000 | 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) | 0.002 | 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".