Improving Referral of Patients for Consideration of Adjuvant Chemotherapy after Surgical Resection of Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Clinical trials demonstrate improved survival for patients with completely resected non-small-cell lung cancer (nsclc) who receive adjuvant chemotherapy. Concerns have been raised about the implementation of those data. The present study measured rates of referral for adjuvant chemotherapy and barriers to referral, and it also evaluated a knowledge translation strategy to change practice. METHODS: An audit and feedback approach was used. Using a retrospective cohort of patients undergoing thoracotomy at St. Joseph's Hospital in Hamilton, Ontario, during January-December 2008, anonymized data were presented to a group of thoracic surgeons for evaluation and feedback. RESULTS: Among 150 thoracotomies performed, 55 patients with nsclc were potentially eligible for adjuvant chemotherapy, but only 27 (49%) were referred for it. Significant variability in referral between surgeons (19%-100%) was observed. Reasons for non-referral were poorly documented in the medical record, but appeared to be primarily the surgeon's decision. The feedback session with surgeons produced a number of constructive suggestions to implement change in practice. CONCLUSIONS: Our findings suggest that surgeon choice was the most significant barrier to implementation of adjuvant chemotherapy for nsclc. Audit and feedback was a useful knowledge translation strategy. However, longer follow-up is needed to document change in practice.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".