M6 Make every contact count- opportunistic lung cancer screening and smoking cessation at a community diagnostic centre
Bibliographic record
Abstract
Introduction Lung cancer screening (LCS) reduces lung cancer mortality and health inequality. Implementation is challenging at a time when screening invites are limited. We sought to establish the numbers of patients attending our community diagnostic centre (CDC) for lung function testing (LFT) that would qualify for opportunistic LCS and smoking cessation services (SCC). Methods Patients attending the CDC for LFT from November 2024 to January 2025 were assessed by age and smoking status to determine whether they would qualify for LCS (55–74 years and current or ex-smokers) and/or referral to tobacco dependency services (TDS). Results In the 3 months 1255 LFT attendances were recorded of which 316 patients within the LCS age range (25.2%, figure 1). Of those 149 (47%) were females and 167 (54%) males, with a median age of 65 yrs. 105 patients were current smokers warranting referral to tobacco dependency services, representing 8.4% of total LFT and 33.2% of within LCS age range. Conclusion One quarter of LFT referrals are within the age range for LCS, representing a group of patients that could be referred directly to LCS from the community (although we did not confirm whether any were already within the programme). 50% of that group were current smokers supporting the need for reliable access to SCC from the CDC. Further work aims to develop easy to use (e.g. QR code) information for CDC attendees on LCS and Smoking cessation with proactive referrals. Furthermore, we propose to assess all attendees at CDC to more broadly understand opportunistic LCS and TDS referrals.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".