PD67 Real-World Evidence To Inform Reflexive Practice And Create Value In Lung Cancer Care
Bibliographic record
Abstract
Introduction Health technology assessment (HTA) agencies assess the value of innovative therapies and publish recommendations for practice. However, is publishing HTA products sufficient to generate value in the real world? The objectives of our work were to: (i) determine whether innovative therapies for lung cancer produce the expected results in the real-world setting; and (ii) assess whether recommendations are followed in real-world practice. Methods Clinical administrative data were used in this two-phase project. In the first phase, a descriptive portrait of the use of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) for treating lung cancer was produced. Their value was assessed by comparing overall survival of treated patients observed in the province of Québec to the published literature. The second phase focused on the initial evaluation of patients diagnosed with lung cancer and treated first by surgery. The delay between first evidence of cancer and surgery was assessed, and the utilization of 27 healthcare services was analyzed and assessed according to our recommendations (algorithms) for lung cancer management. Results From the date the first EGFR-TKI was listed, it took about five years before these drugs were fully integrated into clinical practice. The median overall survival of patients in Québec who used an EGFR-TKI (three indications) was similar to that in most published studies, supporting previous reimbursement decisions. The median delay between first evidence of cancer and surgery was longer than the 60-day consensus target. Utilization of most healthcare services was heterogeneous between regions. Bronchoscopy on its own seemed overused in many regions, whereas non-surgical approaches as a first method for invasive mediastinal evaluation should have been more systematically applied. Conclusions At a relatively low cost, real-world evidence can serve as a powerful tool to validate reimbursement decisions and measure the state of clinical practice. By sharing results with stakeholders, it will enable clinical teams to reflect upon their practice and implement local improvement strategies.
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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 | 0.204 | 0.374 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".