Referrals to a Phase I Clinic and Trial Enrollment in the Molecular Screening Era
Bibliographic record
Abstract
BACKGROUND: Enrichment of patients based on molecular biomarkers is increasingly used in early phase clinical trials. Molecular profiling of patients with advanced cancers can identify specific genomic alterations to inform decisions about investigational treatment(s). Our aim was to evaluate the outcomes of new patient referrals to a large academic solid tumor phase I clinical trial program after the implementation of molecular profiling. MATERIALS AND METHODS: Retrospective chart review of all new referrals to the Princess Margaret Cancer Centre (PM) phase I clinic from May 2012 to December 2014. Molecular profiling using either MALDI-TOF hotspot mutation genotyping or targeted panel DNA sequencing was performed for patients at PM or community hospitals through the institutional IMPACT/COMPACT trials. RESULTS: ≤ .001) were independently associated with successful trial enrollment in multivariable analysis. CONCLUSION: Although nearly half of new patients referred to a phase I clinic had prior molecular profiling, the proportion subsequently enrolled into clinical trials was low. Prior molecular profiling was not an independent predictor of clinical trial enrollment. IMPLICATIONS FOR PRACTICE: The landscape of oncology drug development is evolving alongside technological advancements. Recently, large academic medical centers have implemented clinical sequencing protocols to identify patients with actionable genomic alterations to enroll in therapeutic clinical trials. This study evaluates patient referral and enrollment patterns in a large academic phase I clinical trials program following the implementation of a molecular profiling program. Performance status and referral from a physician within the institution were associated with successful trial enrollment, whereas prior molecular profiling was not an independent predictor.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".