A 10-year history of lung cancer practice guideline development: Process, productivity and impact
Bibliographic record
Abstract
17044 Background: CCO's Program in Evidence-based Care at McMaster University, has developed and disseminated clinical guidance documents through provincial DSGs for 10 years. The 37 member Lung DSG includes medical oncologists (17), radiation oncologists (11), thoracic surgeons (4), nurses (2), and a research coordinator. Pathologists (3), community/patient representatives (2), a medical resident and medical sociologist have previously been members. Methods: The LDSG has used the practice guideline (PG) development cycle described by Browman GP et al (JCO 1998; 16(3):1226–31). Results: 31 reports have been published in peer-reviewed journals, including 25 guidelines; all PGs are posted on the CCO website. Topics were initially selected on the basis of known practice variability (chemotherapy for Stage IV NSCLC) or clinical controversy (combined modality therapy for Stage III NSCLC); PGs for single chemotherapy drugs (6) or chemotherapy usage in specific situations (7) have dominated DSG activity; 5 PGs on radiotherapy alone and 3 on RT as part of CMT have been completed; recent PGs have been written for rare tumours (mesothelioma, thymoma) and diagnostic imaging (PET). Initially, PGs were based solely on published RCT evidence. Evidence from publicly accessible abstracts/meeting presentations, and Phase II trials (in the absence of higher quality evidence) is now considered. For rare tumours (thymoma), the DSG has used a Delphi consensus methodology. Knowledge transfer occurs through the DSG meeting process (twice yearly face-to-face; 2–4 teleconferences), practitioner feedback (PF), publications, presentations and web posting. PF using a standardized feedback questionnaire is generally high (59.9%) but varies by PG and discipline; PF is incorporated into final guideline documents. Guideline recommendations for the use of vinorelbine, gemcitabine, taxanes and erlotinib in NSCLC have been successful in securing government funding. No significant financial relationships to disclose.
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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 | 0.176 | 0.329 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".