Bibliographic record
Abstract
━━ Objective.According to the CONCORD study, the worldwide surveillance of cancer survival, fiveyear net survival of Japanese lung cancer patients is 30.1％, the highest among developed countries in 2005-2009.We aimed to examine the factors accounting for why the survival of lung cancer in Japan is higher than that observed in other countries.Methods.We analysed population-cancer registry data obtained in Japan (J-CANSIS data, collaborative study of six prefectural registries) and compared lung cancer survival based on the Japanese data with those obtained from the ICBP (International Cancer Benchmarking Partnership, Australia, Canada, Denmark, Norway, Sweden and UK participating nations) data.We also examined differences in survival and the distribution of prognostic factors, such as sex, stage, and histological type.Results.In Japan, the 10-year relative survival was higher in women than in men and improved significantly in women.Compared with the ICBP results, the one-year survival of non-small cell and small cell lung cancer in Japan was higher than that seen in the ICBP countries.The difference in the one-year survival of non-small cell lung cancer was large, and notable differences were observed in the stage-specific analysis.The rate of adenocarcinoma in the non-small cell lung cancer patients in Japan was also larger than that observed in the ICBP countries, whereas fewer cases of distant metastasis were noted in the ICBP countries.Conclusions.The early detection of adenocarcinoma using CT scans in Japan may therefore positively contribute to the higher survival rates of lung cancer observed in Japan versus other countries.(JJLC.2015;55:266-272) KEY WORDS ━━ Cancer survival, International comparison, Population-based cancer registry 要旨 ━━ 目的．がん生存率の国際共同調査 CONCORD study(2005-2009 年診断症例)において，肺がんの 5 年相対生存率は，日本は 30.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".