[Systematic Review of the Methodology Quality in Lung Cancer Screening Guidelines].
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the most common malignancy and screening can decrease the mortality. High quality screening guideline is necessary and important for effective work. Our study is to review and evaluate the basic characteristics and methodology quality of the current global lung cancer screening guidelines so as to provide useful information for domestic study in the future. METHODS: Electronic searches were done in English and Chinese databases including PubMed, the Cochrane Library, Web of Science, Embase, CNKI, CBM, Wanfang, and some cancer official websites. Articles were screened according to the predefined inclusion and exclusion criteria by two researchers. The quality of guidelines was assessed by AGREE II. RESULTS: At last, a total of 11 guidelines with methodology were included. The guidelines were issued mainly by USA (81%). Canada and China developed one, respectively. As for quality, the average score in the "Scale and objective" of all guidelines was 80, the average score in the "Participants" was 52, the average score in the "rigorism" was 50, the average score in the "clarity" was 76, the average score in the "application" was 43 and the average score in the "independence" was 59. The highest average score was found in 2013 and 2015. Canada guideline had higher quality in six domains. 7 guidelines were evaluated as A level. CONCLUSIONS: The number of clinical guidelines showed an increasing trend. Most guidelines were issued by developed countries with heavy burden. Multi-country contribution to one guideline was another trend. Evidence-based methodology was accepted globally in the guideline development.
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 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 | 0.073 | 0.285 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".