Bibliographic record
Abstract
Introduction:The effectiveness of knowledge translation (KT) interventions applied to implementation of lung cancer screening (LCS) programmes using low-dose CT scans is unclear.This systematic review addresses KT strategies used to increase participation in LCS.Methods: A PICO framework was designed.Literature searches were performed for studies incorporating KT strategies in relation to LCS.Searches were performed in MED-LINE, EMBASE, CINAHL, Cochrane, Web of SCIENCE and Scopus.Selection and consensus was performed by 2 reviewers.Included studies had to utilize a KT intervention intended to facilitate participation in screening, improve intention to screen, or increase screening uptake.Results: 1160 studies were identified for title and abstract review.After applying inclusion and exclusion criteria, 112 were selected for full text review and then 24 s for data extraction.Studies originated from USA (20), Canada (1), UK (2) and Japan (1), published between 2014 and 2021.KT interventions included staff training and patient education (classes, print, web-based and video), shared decision making tools, forms (online and paper), reminders and triggers, data presentation modalities, and materials targeting specific populations.In relation to the KT intervention, there were 5 studies that addressed knowledge based endpoints to facilitate screening participation; 8 studies that addressed intention to screen endpoints; and 11 studies that addressed actual LCS uptake.Of these 11 studies, 4 demonstrated a positive effect on lung cancer screening rates after the KT intervention; 3 showed no effect; and 4 had no comparator.Contribution: This systematic review identified several studies that addressed the utilisation and effectiveness of various KT interventions in the context of LCS.Most of these were low level evidence.Randomised controlled trials that measured actual screening rates as an outcome were lacking.It is important that KT interventions are explored through high quality studies in order to optimise the implementation of LCS programmes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".