Designing a web application for personalized medicine trials.
Bibliographic record
Abstract
e13107 Background: Clinical genomics uses information from a patient's genome in clinical decision-making, an example of personalized medicine. The OICR/UHN Genomics Cohort Study is assessing the feasibility and developing standard operating procedures for clinical genomics in late stage cancer patients to enable larger trials. Tumor DNA from consenting patients is sequenced across 19 genes to identify actionable mutations and inform use of targeted therapeutic agents. Informatics systems are critical as the study involves 40 staff in 5 cancer centres, 2 laboratories, 3 genomics technologies, and spans screening, consent, obtaining/processing biopsy samples, genomic analysis, clinical laboratory verification, and reporting for decision-making. Methods: We used a process-centered method to develop a web system to manage study activities. Initially it tracked patients, samples, genomic results, decisions and reports across the cohort, monitored progress and sent reminders, working alongside an electronic data capture (EDC) system for the trial's clinical and genomic results. We later added a system to read, store, and analyze the genomics data, and a knowledge base of mutations’ tumor frequency (from the COSMIC database) annotated with clinical significance and drug sensitivity to generate reports for clinicians. Results: The web tracker proved highly adaptive. The design method allowed procedural refinements mid-study, including changes in sample preparation, sample sources, and differences in nomenclature across technologies. As the study procedures stabilized, the system provided deeper support for clinical decision making, enabling the generation of draft reports for verification by an expert panel prior to forwarding to the treating physician. The web tracker complemented the EDC system with its fixed modules for collection of clinical data and genomic results. Conclusions: The system effectively complemented clinical trial software. An agile development process enabled procedures to be refined as feasibility issues were found and resolved, and enabled flexible analysis of mutation data. Our design approach helped stabilize effective procedures for a clinical genomics service, and establish means to assess its performance.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.028 |
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".