Impact of a Permission to Contact (PTC) Platform on Biobank Enrollment and Efficiency
Bibliographic record
Abstract
The consent process involves three steps; referral for contact, preliminary interview, and informed consent discussion. We propose that the efficiency and frequency of the consent process for individual biobank related projects increases when the referral for contact is conducted by an independent "Permission to Contact" (PTC) platform within a health research organization. A PTC platform established at our center in 2007 obtains "permission to be contacted about future cancer research" from approximately 1200 patients annually. With ethics board approval, the British Columbia (BC) Cancer Agency's Tumour Tissue Repository (TTR) deployed a post-procedure consent protocol designed to obtain initial referrals from the PTC platform. This protocol was initially deployed for breast and gastrointestinal (GI) cancer patients (48% of patients), and later expanded as an option for all patients. We examined the impact on biobank accrual over a 4-year period spanning implementation of the post-procedure protocol. Within the first 2 years, while deploying an existing pre-procedure consent protocol, the TTR received, on average, 38.5 referrals/month, and consented 36.5 patients/month. Over the next 24 months, referral and consent rates increased to 68.5/month and 45.6/month, respectively, while operating both pre-procedure and post-procedure protocols. This represents a significant increase in overall referrals (1.78 fold) and consented patients (1.25 fold). For breast and GI cancer patients, referrals and consents, increased even further (2.4 and 1.6 fold, respectively). Overall, the consented/declined/unknown decision rates in the first period were 95.3%/1.2%/3.5% (n=918 approached patients), while rates in the second period were 86%/2.3%/11.7% (n=1272 approached patients). Overall, consent process costs fell by 14% per case. Patient engagement can be positively influenced by connecting a biobank with a PTC platform enhancing efficiency in obtaining consent, which is a key determinant of tumor biobank costs.
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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 | 0.227 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".