Novel Methodology for Comparing Standard-of-Care Interventions in Patients With Cancer
Bibliographic record
Abstract
PURPOSE: The current clinical trials development and conduct process is cumbersome and expensive, with the majority of studies focusing on either the development of new agents or new indications for established agents. Unfortunately, research comparing standard-of-care interventions is rarely performed, leaving many important and practical patient-centered questions unanswered. Novel clinical trial methodologies and approaches are needed. METHODS: We have identified simple key components that, when combined, enhance the ability to both perform and increase accrual for studies that compare standard-of-care interventions. These include selection of clinically relevant and practical questions, demonstration of clinical equipoise through surveys of knowledge users and completion of systematic reviews, appropriate study design and simply defined study end points, use of an integrated consent model incorporating oral consent, efficient research ethics board approval, Web-based randomization in the clinic, real-time electronic data capture and management, and regular formal team feedback. RESULTS: We have demonstrated the feasibility of this model in a pragmatic trial comparing two standard-of-care interventions (growth factor support or ciprofloxacin) for the primary prophylaxis of febrile neutropenia in patients with breast cancer receiving adjuvant docetaxel with cyclophosphamide chemotherapy. Research ethics board approval took 3 months, and 110 (72%) of 153 potentially eligible patients have agreed to participate in the study. When surveyed, 81 (85%) of 95 patients were completely satisfied with the integrated consent model process. CONCLUSION: Our proposed model contains elements that, when used alone or in combination, may allow efficient and cost-effective comparison of standard-of-care interventions.
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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 | 0.566 | 0.676 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".