Use of Period Analysis to Timely Assess Five-Year Relative Survival for the Patients With Bone Cancer
Bibliographic record
Abstract
Background: While timely assessment of long-term survival for patients with bone cancer is essential for evaluation on early detection and prognosis level of treatment of bone cancer, those data are extremely scarce in China. We aimed to timely and accurately assess long-term survival for patients with bone cancer in Eastern China. Methods: Patients diagnosed with bone cancer during 2004 - 2018 from four cancer registries with high-quality data from Taizhou, Eastern China were included. Five-year relative survival (RS) of bone cancer patients was calculated by period analysis for overall and the stratification. We further predicted 5-year RS during upcoming 2019 - 2023 using a model-based period analysis and survival data during 2004 - 2018. Results: Overall, 5-year RS for patients with bone cancer during 2014 - 2018 reached 46.6%, being 40.8% for male and 51.0% for female. Five-year RS declined along with aging, decreasing from 58.9% for age < 45 years to 41.5% for age > 60 years, while 5-year RS for urban area was higher compared to rural area (59.1% vs. 44.3%). The 5-year RS during upcoming 2019 - 2023 reached 48.3%. We found a clear upward trend in 5-year RS during 2004 - 2023 for overall and the stratification by sex, age at diagnosis, and region. Conclusions: We found that, for first time in China using period analysis, most up-to-date 5-year RS for patients with bone cancer reached 46.6% during 2014 - 2018, and is projected to reach 48.3% for the period 2019 - 2023, which has important implications for timely evaluation on early detection and prognosis level of treatment for patients with bone cancer in Eastern China.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".