Bibliographic record
Abstract
Objective To review and evaluate the basic contents and development of the current global clinical guidelines for lung cancer practice so as to provide useful information for domestic study. Methods Six databases including PubMed (to June 2008) and relevant websites (both in Chinese and English) were searched. Articles were screened according to the prede ned inclusion and exclusion criteria. The number of clinical guidelines was counted and the quality of guidelines was also assessed. Results A total of 208 articles were found to be clinical guideline-related and 133 were nally included. Of those, 78 were original versions and 55 were updated versions. And 86 articles mentioned guideline development methodology. The guidelines were issued by 14 countries/regions, mainly by USA (39.85%), Canada (24.81%) and France (8.27%). The earliest one was published by USA in 1984. 125 guidelines were issued by oncological or thoracic institutions, and the other 8 were not issued by specialized institutions or not speci ed. The 133 articles were classi ed into 3 major clinical categories: synthesis (24), multi-subject (21) and single-subject (88). As for quality evaluation, the average score of all guidelines was 72.09 (full score 100). The highest average score was found in 1996 which was 83.50, and the lowest in 1997 (66.80). The guidelines issued by France had the highest average score (79.80), and Japan, with the lowest average score (48.00). The average score of 4 categories of lung cancer were 73.54 (non-small- cell lung cancer), 65.74 (lung cancer), 74.72 (small-cell lung cancer), and 76.00 (bronchogenic lung cancer), respectively. Conclusion The number of clinical guidelines showed an increasing trend. Most guidelines were issued by developed countries. The subjects included in the synthetic guidelines showed an expanding trend covering about 20 subjects from prevention to palliative care. A trend of multi-country contribution to the guidelines development and revision was noted.Researches became more focused on di erent types and stages. Evidence-based methodology was accepted globally in the clinical guideline development, but unfortunately very few applied the method of health technology assessment. China issued only 2 original guidelines, which were based on literature review and expert opinions, respectively. Due to the limitation of language restriction, inaccessibility of full-text articles and unavailability of authorized and speci c quality evaluation protocols, the conclusions of this study should be interpreted with caution.
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 | 0.074 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.027 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".