PD58 Implementation And Assessment Of A Lung Cancer Screening Pilot Project In Québec Through Multi-Stakeholder Collaboration
Bibliographic record
Abstract
Introduction In 2019, the Québec provincial health technology assessment body (INESSS) recommended that lung cancer screening with low-dose computed tomography (LDCT) be accessible in Québec only within the context of an evaluation in the ‘real-world’ care setting. Based on this recommendation, the ministry of health (MSSS) decided, in 2020, to implement a screening pilot project and to conduct a formal evaluation, partnering with a clinical leader (principal investigator), participating hospitals, the provincial public health agency (INSPQ) and INESSS. The goal of this evaluation is to facilitate decision-making regarding the implementation of a province-wide screening program. Methods To support the implementation of the pilot project, algorithms and recommendations were developed to guide management of screening program participants. This material, based on Lung-RADS (Lung Computed Tomography Screening Reporting and Data System of the American College of Radiology), was developed by reviewing the literature and by consulting clinical experts. The evaluation plan proposes various indicators, focusing on six main topics: (i) costs, (ii) screening and investigation processes, (iii) clinical effectiveness and other effects on health, (iv) effects on smoking cessation, (v) organizational impact and (vi) implementation issues. Results INESSS has developed 12 algorithms and close to 50 recommendations for lung cancer screening and investigation, a tool for assessing lung cancer risk and a benefits/risks table. For the evaluation of the pilot project, MSSS, INSPQ and INESSS developed more than 100 indicators; short-term indicators are currently being measured and others will follow in the longer term. Since starting in June 2021, the pilot project is progressing well (as of November 28, 2021): 2,365 people have been referred, 1,272 were eligible for screening, 678 have had their first LDCT and 19 were Lung-RADS 4B or 4X. Results on indicators will help the ministry decide on the feasibility of scaling up screening to the provincial level and will highlight aspects to be improved. Conclusions This project shows how health technology assessment products can elicit changes in the health system, and how multi-stakeholder collaboration can actively support practice implementation and inform decision-making.
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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 | 0.059 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".