An evaluation of administrative data linkage for measurement of real-world outcomes of large clinical panel sequencing for advanced solid tumors.
Bibliographic record
Abstract
283 Background: There is limited real-world evidence of impact of large clinical panel sequencing on treatment-matching for patients with advanced solid tumors. The province of Ontario has a single payer, publicly funded health care system. We linked genomic testing results from a prospective province-wide trial, OCTANE (Ontario-Wide Cancer TArgeted Nucleic Acid Evaluation), to administrative data to determine the feasibility of this approach for evaluating survival and the impact of sequencing on treatment matching. Methods: We linked all Ontario patients from Princess Margaret (PM) with panel testing results (tumor-only 555-gene panel) to province-wide administrative data on treatments and outcomes. Patients were recruited from August 2016 to August 2018. Only clinically actionable variants based upon OncoKB annotation (Level 1 and 2) were assessed for genotype-informed treatment matching. Results: All 888 eligible patients were successfully linked to administrative data. Mean age was 58 (±13) years, 635 (71.5%) were female. Most common disease sites were ovary (26.4%), uterus (14.0%), colorectal (11.8%) and breast (9.5%). Administrative data vital status was more complete than trial collected data with 262 of 476 deaths only recorded in administrative data. Median survival was 1.70 years (95% confidence interval 1.50-1.91). 247 (27.8%) had actionable mutations, most commonly PIK3CA (54.7%), BRCA1 (15.8%), BRCA2 (15.0%) and BRAF (8.9%). 37 (15.0%) and 42 (17.0%) patients with actionable mutations received targeted therapy within 6 and 12 months of test report date, respectively. Conclusions: This is the first known feasibility study of linked administrative data to measure outcomes of large clinical panel sequencing for patients with advanced solid tumors. Vital status was more complete with administrative data compared to trial-collected data, and treatment data was successfully linked. About one in twenty-one enrolled patients received genome-informed treatments within 12 months, or about one in six of all patients with actionable mutations. This may be due to short interval follow up, trial and drug access, successful standard of care treatments, early patient deterioration or limited alterations covered by the panel, among other causes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.257 | 0.439 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".