Feasibility pilot implementation of a low-dose CT screening program.
Bibliographic record
Abstract
109 Background: The National Lung Cancer Screening Trial showed 20% reduction in lung cancer mortality with low dose CT (LDCT) screening. The highest risk populations for developing lung cancer are commonly found among lower socioeconomic groups. 30% of patients enrolled with St. Michael’s Hospital Academic Family Health Team (SMHAFHT) are below the low-income cut-off. This study aims to implement LDCT screening at SMHAFHT using the Knowledge-to-Action framework. Methods: Stakeholders were engaged to adapt an existing Princess Margaret Cancer Centre (PMCC) LDCT program with focus groups, interviews, and patient post-LDCT interviews to assess barriers and facilitators. Patients were referred from SMHAFHT to PMCC for LDCT. PLCO2012 scores stratified patient risk, and those at highest risk were chosen. Results: 12 patients were assessed for eligibility; 7 (56%) qualified and 5 (42%) completed LDCT at PMCC. 11 (92%) patients assessed were from two Smoking Cessation Clinics (SCC) within SMHAFHT. Those at SCC (vs LDCT) group were younger (51±12 vs 69±7, p = 0.001), had lower Charlson comorbidity index scores (2.1±1.8 vs 3.4±1.7, p = 0.15) and had fewer pack years (37±24 vs 56±16, p = 0.06). The SCC (vs LDCT) group had comparable prevalence of males (64% vs 66%), COPD (30% vs 33%), personal cancer history (5% vs 0%), family history of lung cancer (17% vs 20%), and proportion of patients reporting the lowest quartile of household income (95% vs 80%), all p > 0.05. Thematic analysis yielded the following barriers among practitioners: limited study awareness, difficulty providing consent forms and lack of time. Top patient reported barriers included fear: of positive results, LDCT and follow up procedures. Practitioner facilitating factors included greater research coordinator involvement, while patients reported clear communication of LDCT risks and benefits and accessibility of PMCC. Conclusions: Implementation predominantly took place within SMHAFHT SCCs. Barriers for future intervention were identified. Differences between participants of SCC and LDCT screening can be attributed to selection of the highest-risk group with PLCO2012 scores. Implementing LDCT screening at SCCs allowed for the recruitment of those below the low-income cut-off.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".