Incidences of Primary Soft Tissue Sarcoma Diagnosed on Extremities and Trunk Wall
Bibliographic record
Abstract
Most epidemiological studies of soft tissue sarcoma (STS) were performed in the Western countries, and only limited data highlighting that in the Asian population. The aim of this study is to conduct a comprehensive analysis for the incidence rates of STS in Taiwan.This was a population-based study analyzing the incidence rates and trends of the primary STS over extremities and trunk wall during 2003 to 2011 by using the nationwide Taiwan Cancer Registry. More specific analyses were conducted for subtypes. Incidence rates of overall STS by cities and counties were also investigated.A total of 3843 cases were diagnosed with STS during the study period, giving an age-standardized rate (ASR) of 1.63 per 100,000 person-years. Liposarcoma was the most frequent subtype, followed by undifferentiated pleomorphic sarcoma and leiomyosarcoma. STS was more frequently diagnosed in males and angiosarcoma was the most prominent sex-specific type. ASR increased with age in most of the STS subtypes and varied by histologic subtype. The incidence of peripheral primitive neuroectodermal tumor was highest in children, whereas rhabdomyosarcoma revealed a bimodal age distribution. Annual percent change (APC) of STS was 2.2%, and significant change in trend was only in males (APC, 3.5%, P < 0.05). Geographical variations indicated that New Taipei City had a significantly higher rate compared with the rest areas. Significantly lower rates were observed in 1 major offshore island.Incidence variations of STS by sexes, ages, histologic subtypes, and geographic regions were observed in Taiwanese population. The emerging factors associated STS incidence rates deserve further studies to verify.
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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 | 0.001 |
| 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".