Initial Treatment Modalities in Patients with Newly Diagnosed Primary Lung Cancer in Japan
Bibliographic record
Abstract
Treatment for lung cancer continues to rapidly evolve. Here, we describe trends in the initial treatment of adults with newly diagnosed primary non-small-cell lung cancer in Japan. This retrospective cohort study used data from JMDC Inc. Claims Database from 2015 to 2023. Adults with lung cancer, confirmed using a combination of diagnosis, treatment, or procedure codes, were enrolled. A total of 9373 patients were included, with a mean age of approximately 59 years. The median time from diagnosis to treatment initiation ranged from 38 days in patients treated surgically to 25 days in patients with distant metastases. The observed trends were a decrease in the percentage of newly diagnosed patients with distant metastases, a decline in chemotherapy use in patients with early-stage disease, and in advanced disease, a more than doubling in the use of targeted therapy, including checkpoint inhibitors, while radiotherapy and chemotherapy tended to decrease. The observed changes in treatment were driven mainly by the increased use of targeted therapies including checkpoint inhibitors and are aligned with current treatment guidelines in Japan. The observation of fewer patients with distant metastases over time possibly indicates earlier detection. Additional research is needed to understand if new therapies are being extended to older and frail patients with lung cancer in Japan.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".