A mixed methods approach to understand variation in lung cancer practice and the role of guidelines
Bibliographic record
Abstract
INTRODUCTION: Practice pattern data demonstrate regional variation and lower than expected rates of adherence to practice guideline (PG) recommendations for the treatment of stage II/IIIA resected and stage IIIA/IIIB unresected non-small cell lung cancer (NSCLC) patients in Ontario, Canada. This study sought to understand how clinical decisions are made for the treatment of these patients and the role of PGs. METHODS: Surveys and key informant interviews were undertaken with clinicians and administrators. RESULTS: Participants reported favorable ratings for PGs and the evidentiary bases underpinning them. The majority of participants agreed more patients should have received treatment and that regional variation is problematic. Participants estimated that up to 30% of patients are not good candidates for treatment and up to 20% of patients refuse treatment. The most common barrier to implementing PGs was the lack of organizational support by clinical administrative leadership. There was concern that the trial results underpinning the PG recommendations were not generalizable to the typical patients seen in clinic. The qualitative analysis yielded five themes related to physicians' decision making: the unique patient, the unique physician, the family, the clinical team, and the clinical evidence. A dynamic interplay between these factors exists. CONCLUSION: Our study demonstrates the challenges inherent in (i) the complexity of clinical decision making; (ii) how quality of care problems are perceived and operationalized; and (iii) the clinical appropriateness and utility of PG recommendations. We argue that systematic and rigorous methodologies to help decision makers mitigate or negotiate these challenges are warranted.
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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 | 0.141 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".