Systematic review of guidelines for the management of suspected lung cancer in primary care.
Bibliographic record
Abstract
OBJECTIVE: To systematically review the literature and provide an update and integration of existing peer-reviewed guidelines with recent systematic reviews and with primary studies related to the early recognition and management of lung cancer in primary care. DATA SOURCES: MEDLINE and EMBASE were searched for relevant articles. The quality of the evidence to support existing guideline recommendations, and the consistency of recommendations with updated evidence, were assessed. Applicability in a Canadian primary care setting was also evaluated. STUDY SELECTION: All studies that explored signs or symptoms of or risk factors for lung cancer in the primary care setting were included. All diagnostic studies in which symptomatic primary care patients underwent 1 or more investigations were also searched. SYNTHESIS: Recommendations were consistent among guidelines despite a paucity of supporting evidence. Updated evidence provided further support for the recommendations. Recommendations for identifying signs and symptoms of lung cancer presenting in primary care and for initial management can be adopted and applied within a Canadian primary care setting. CONCLUSION: This updated review of recommendations might help promote evidence-based practice and, ultimately, more timely management and improved prognosis for lung cancer patients. It might also assist in the development of lung cancer diagnostic assessment programs.
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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 | 0.024 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| 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".