Readiness of health care systems to generate RWE: Frequency of radiographic imaging of metastatic disease during first-line systemic therapy.
Bibliographic record
Abstract
268 Background: Regulatory and Health Technology Assessment (HTA) agencies are increasingly using real world data (RWD) to support real world evidence (RWE), but the readiness of healthcare systems to reliably generate RWE is unknown. As a quality assurance measure we examined the preparedness of a single payer system to provide RWE by evaluating the frequency of CT imaging during standard first line metastatic systemic treatment of breast, colorectal (CRC) and lung cancer. Methods: A 1-year cohort of de novo metastatic breast, CRC, lung cancer patients treated with first line systemic therapy (excluding hormone therapy) referred to BC Cancer in 2016 was retrospectively reviewed. Duration of first line treatment was calculated from first to last dose of therapy. Baseline CT included imaging within 8 weeks prior to and 3 weeks after treatment initiation (first cycle). Last CT included imaging up to 8 weeks after the last dose of therapy. Results: A cohort of 675 patients was identified from the BC Cancer Registry. The distribution of de novo metastatic disease at diagnosis was lung (n = 379), CRC (n = 214) followed by breast cancer (n = 82). Conclusions: In our publicly funded health care system, baseline CT scans within 4 weeks prior to treatment ranged from 57-72%. The median CT imaging interval during first line metastatic treatment was ranged from 7.9-11.3 weeks. RWD from routine clinical practice differs significantly from clinical trials, the gold standard for regulatory and HTA assessments. Population-based data may contribute to RWE with caution due to limitations imposed by clinical practice. [Table: see text]
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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 | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".