Levels of evidence and grades of recommendations in general thoracic surgery.
Bibliographic record
Abstract
OBJECTIVES: To determine the grades of recommendations and levels of evidence available if the formal practice of evidence-based medicine is applied to general thoracic surgery. METHODS: Three general thoracic surgeons, by consensus, developed a sample of 10 clinically important questions. The first 3 steps of evidence-based medicine (creation of answerable clinical questions, search for best external evidence, and critical appraisal of literature) were performed. Abstracts and appropriate articles were identified through Medline from January 1999 through December 2001. A hierarchical series of search strategies was employed to identify the best level of evidence. The best evidence found was categorized according to the Oxford Centre for Evidence-Based Medicine into 4 grades of recommendations (A-D) and 5 levels of evidence (1-5). RESULTS: The best evidence found for the 10 sample questions was categorized as grade A recommendations in 5 and grade B, also in 5 questions. The levels of evidence found were la in 3 studies, 1b in 5, and 2b in 2. CONCLUSIONS: A formal evidence-based-medicine approach to general thoracic surgery found the grades of recommendation and levels of evidence for a sample of clinically important questions to be high.
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 | 0.116 | 0.366 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.023 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".