Implementation of a consent for chart review and contact and its impact in one clinical centre
Bibliographic record
Abstract
OBJECTIVE: Informed consent and protection of patient confidentiality are central to the conduction of clinical research. Consent for chart review and contact (CCRC) allows a patient chart to be screened for research by persons outside the direct circle-of-care and for the patient to be contacted regarding potential studies. This study describes the process of implementation and benefits of such a consent. DESIGN: We present a descriptive report of a CCRC document that was created and presented to patients over a 3.5-year period at a tertiary care Endocrinology and Metabolism centre. To assess the potential impact of such a document on patient recruitment, the basic demographics of patients who did and did not consent were compared. In addition, we compared the recruitment rate at our centre, using our novel approach, with that at other centres for an ongoing study of patients with type 1 diabetes. RESULTS: A large proportion (6501/8025, or 81%) of patients gave their consent for chart review. Patients who denied consent were more likely to be women and older. Compared with other centres, our centre recruited at the highest rate for a known study of patients with type 1 diabetes. The majority (46/60, or 76.7%) of patients were recruited via the novel approach. CONCLUSIONS: Consent for chart review and contact addresses several important ethical issues regarding the use of patient clinical information for research purposes. Our study demonstrated how such a process can be implemented.
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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 | 0.309 | 0.395 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".