A Permission to Contact Platform Is an Efficient and Cost-Effective Enrollment Method for a Biobank to Create Study-Specific Research Cohorts
Bibliographic record
Abstract
Background: The permission to contact (PTC) platform is a useful mechanism to increase patient engagement and enrollment into biobanks. It provides biobanks with the ability to select specific patient cohorts and to complete consent to facilitate access to biospecimens and data. In this study, we evaluated consenting costs for a biobank to compile a research cohort based on utilizing a PTC platform to obtain consent as compared with utilizing a prospective consenting approach. Methods: In this study, we utilized a PTC platform to conduct an initial selection of potential participants for two breast cancer cohorts and to provide a “referral” to the biobank to recontact these patients to provide consent to access clinical archival biospecimens and associated data. We evaluated the effort, costs, and cohorts compiled by this approach to compare this mechanism with the alternative: compiling the same type of cohorts based on a classic biobank enrollment approach. Results: After initial diagnosis and provision of a PTC up to 12 years before, recontact was possible in 84 of 90 (74%) and 77 of 107 (72%) breast cancer patients for preinvasive (ductal carcinoma in situ [DCIS]) and invasive (triple-negative subtype) cancers. Of those recontacted, consent was completed in 42 of 84 (55%) DCIS patients and 48 of 107 (45%) triple negative breast cancer (TNBC) patients. The total cost of using PTC to recontact patients to compile these two consented cohorts was CAD $26.34 and CAD $20.11 per patient consent, respectively. Conclusions: We have demonstrated the feasibility of utilizing a PTC platform to obtain informed consent from patients for a specific study through referrals provided several years after initial PTC was provided. Depending on the existing biobank operational model and the efficiency of its processes for enrollment and obtaining broad informed consent, the implementation of a PTC platform may be an efficient and cost-effective complementary method for a biobank to enroll patients to develop criteria-specific cohorts to support research.
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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 | 0.086 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.064 | 0.023 |
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".