Bibliographic record
Abstract
Objective To review new evidence reported since the 2016 publication of the Canadian Task Force on Preventive Health Care recommendations and to summarize key facets of lung cancer screening to better equip primary care providers (PCPs) in anticipation of wider implementation of the recommendations. Quality of evidence A new, large randomized controlled trial has been published since 2016, as have updates from 4 other trials. PubMed was searched for studies published between January 1, 2004, and December 31, 2020, using search words including lung cancer screening eligibility, lung cancer screening criteria, and lung cancer screening guidelines. All information from peer-reviewed articles, reference lists, books, and websites was considered. Main message Lung cancers diagnosed at stage 4 have a 5-year survival rate of only 5% and have a disproportionate impact on those with lower socioeconomic status, rural populations, and Indigenous populations. By downstaging, or diagnosing lung cancers at an earlier and more treatable stage, lung cancer screening reduces mortality with a number needed to screen of 250 to prevent 1 death. Practical aspects of lung cancer screening are reviewed, including criteria to screen, appropriate low-dose computed tomography screening, and management of findings. Harms of screening, such as overdiagnosis and incidental findings, are discussed to allow PCPs to appropriately counsel their patients in the face of ongoing implementation of new lung cancer screening programs. Conclusion Lung cancer screening, with its embedded emphasis on smoking cessation, is an excellent addition to PCPs’ preventive health care tools. The implementation of formal and pilot lung cancer screening programs across Canada means that PCPs will be increasingly required to counsel their patients around the uptake of lung cancer screening.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.165 | 0.071 |
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".