Consenso de recomendaciones para tamizaje del cáncer pulmonar en Chile
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer death worldwide, also in Chile, where the main risk factor is smoking. Early detection using low-dose computed tomography has been shown to reduce mortality from this cause, but there are still no formal screening recommendations in Chile. AIM: This initiative aimed to develop recommendations for lung cancer screening in Chile through an expert consensus process. METHODS: A mixed approach was used in this study. A literature review was conducted to identify recommendations. A group of 18 Chilean experts was selected to form the Delphi panel. The RAND/UCLA method and evidence-based decision-making criteria were used to guide the evaluation process. Three rounds of voting and one synchronous meeting were conducted. RESULTS: Following a literature review and expert evaluation, 25 recommendations relevant to lung cancer screening in Chile were selected. These recommendations cover the selection of the target population, the frequency and duration of the program, and the implementation and characteristics of the programs and the health institutions that would host them. CONCLUSIONS: Experts expressed concerns about the feasibility and acceptability of implementing screening programs in the local context, noting possible barriers, such as lack of awareness and costs. Local evidence needs to be generated to address the regional particularities and ensure the feasibility and effectiveness of lung cancer screening programs in Chile.
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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 | 0.061 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".