From randomized trials to the clinic: is it time to implement individual lung-cancer screening in clinical practice? A multidisciplinary statement from French experts on behalf of the french intergroup (IFCT) and the groupe d'Oncologie de langue française (GOLF)
Bibliographic record
Abstract
BACKGROUND: Despite advances in cancer therapy, mortality is still high except in early-stage tumors, and screening remains a challenge. The randomized National Lung Screening Trial (NLST), comparing annual low-dose computed tomography (LDCT) and chest X-rays, revealed a 20% decrease in lung-cancer-specific mortality. These results raised numerous questions. The French intergroup for thoracic oncology and the French-speaking oncology group convened an expert group to provide a coherent outlook on screening modalities in France. METHODS: A literature review was carried out and transmitted to the expert group, which was divided into three workshops to tackle specific questions, with responses presented in a plenary session. A writing committee drafted this article. RESULTS: The multidisciplinary group favored individual screening in France, when carried out as outlined in this article and after informing subjects of the benefits and risks. The target population involves subjects aged 55-74 years, who are smokers or have a 30 pack-year smoking history. Subjects should be informed about the benefits of quitting. Screening should involve LDCT scanning with specific modalities. Criteria for CT positivity and management algorithms for positive examinations are given. CONCLUSIONS: Individual screening requires rigorous assessment and precise research in order to potentially develop a lung-cancer screening policy.
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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 | 0.729 | 0.772 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.031 | 0.026 |
| Open science | 0.015 | 0.012 |
| Research integrity | 0.064 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".