Incidental findings during lung low‐dose computed tomography cancer screening in Australia and Canada, 2016–21: a prospective observational study
Bibliographic record
Abstract
OBJECTIVES: To investigate the type and frequency of incidental findings in people at high risk of lung cancer who undergo baseline low-dose computed tomography (LDCT) lung cancer screening in Australia and Canada. STUDY DESIGN: Prospective observational study; sub-study of the single-arm International Lung Screen Trial (ILST) lung cancer screening study. SETTING, PARTICIPANTS: risk prediction model; or a smoking history of 30 pack-years or more). Initial LDCT screening was undertaken at one of five participating hospitals in Australia and one in Canada. MAIN OUTCOME MEASURES: Prevalence of incidental findings during baseline LDCT lung cancer screening (using a research checklist), by country, classified by experienced radiologists as requiring or not requiring clinical follow-up; reporting of incidental findings in clinical reports for treating physicians (two Australian sites only). RESULTS: A total of 4403 participants completed baseline LDCT screening at the six participating hospitals. The mean age (64-65 years) and the proportions of participants who currently smoked (47-55%) were similar at all six sites; the proportion of female participants was larger in Sydney (52%) and Vancouver (51%) than the other sites (39-44%). At least one incidental finding was made during baseline LDCT screening of 3225 people (72.8%); findings in 454 people (10.3%) required clinical follow-up. The most frequent incidental findings were coronary artery calcification (3022 of 4380 participants with recorded results, 69.0%) and emphysema (2378 of 4401, 54.0%). Marked differences between the Australian and Canadian sites in the prevalence of incidental findings were noted, and also between the two Australian sites in their communication of incidental findings in clinical screening reports. CONCLUSION: Incidental findings during lung cancer screening were frequent, and clinical reporting of these findings was inconsistent. When LDCT lung cancer screening is introduced in Australia, a standardised reporting template should be used to provide clear guidance about the clinical significance of such findings. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02871856 (prospective, 18 August 2016).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".