PD24 Data Collection By Patient Groups To Provide Patient Input
Bibliographic record
Abstract
Introduction: The Canadian Agency for Drugs and Technologies in Health (CADTH) Common Drug Review and pan-Canadian Oncology Drug Review programs incorporate perspectives and experiences from patients and family members who might be affected by the resulting funding recommendation. Perspectives are provided by patient groups who use different approaches to gather patient input. Methods: We analyzed a random sample of ninety-three patient input submissions, drawn from a sampling frame of 532 submissions given to CADTH between June 2010 and June 2016. We looked at how groups described their information gathering methods in the original submissions or the published Clinical Guidance Reports. Results: Approaches were categorized according to whether they involved primary (n = 86) or secondary data collection (n = 130) and further sub categorized according to how data was collected. Primary data included: personal experiences, as described by the submission's author (n = 16); surveys conducted specifically for the submission (n=34); and new interviews of patients and family members on disease and drug experiences (n = 36). Half (forty-seven of ninety-three) of the patient input submissions included experiences of one or more patients who had received the drug under review. Secondary data included: published literature (n = 31); existing surveys (n = 27); past conversations with patients and family members (n = 36); experiences of patient group staff interacting with patients and family members (n = 19); and advice from clinical experts (n = 17). Many patient input submissions (sixty-eight out of ninety-three) reported multiple approaches to collect data. Use of two approaches was most common (thirty-seven out of ninety-three) with five or six approaches used in three of ninety-three submissions. Conclusions: Despite resource and timing challenges, many patient groups gather primary data to share with CADTH and find individuals with experience of the drug under review.
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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 | 0.095 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".