A Rapid Outcomes Ascertainment System Improves the Quality of Prognostic and Pharmacogenetic Outcomes from Observational Studies
Bibliographic record
Abstract
PURPOSE: Case-control and observational studies are popular choices for evaluating molecular prognostic/pharmacogenetic outcomes, but data quality is rarely tested. Using clinical trial and epidemiologic methods, we assessed the quality of prognostic and outcomes data obtainable from a large case-control study of lung cancer. METHODS: We developed an explicit algorithm (set of standard operating procedures forming a rapid outcomes ascertainment system) that encompassed multiple tests of quality assurance, and quality of data for a range of prognostic and outcomes variables, in several cancers, across several centers and two countries were assessed. Based on these assessments, the algorithm was revised and physicians' clinical practice changed. We reevaluated the quality of outcomes after these revisions. RESULTS: Development of an algorithm with internal quality controls showed specific patterns of data collection errors, which were fixable. Although the major discrepancy rate in retrospective data collection was low (0.6%) when compared with external validated sources, complete data were found in <50% of patients for treatment response rate, toxicity, and documentation of patient palliative symptoms. Prospective data collection and changes to clinical practice led to significantly improved data quality. Complete data on response rate increased from 45% to 76% (P = 0.01, Fisher's exact test), for toxicity data, from 26% to 56% (P = 0.02), and for palliative symptoms, from 25% to 70% (P < 0.05), in one large lung cancer case-control study. CONCLUSIONS: Observational studies can be a useful source for studying molecular prognostic and pharmacogenetic outcomes. A rapid outcomes ascertainment system with strict ongoing quality control measures is an excellent means of monitoring key variables.
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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 | 0.422 | 0.607 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".