Challenges of post‐authorization safety studies: Lessons learned and results of a French study of fentanyl buccal tablet
Bibliographic record
Abstract
PURPOSE: Recruiting and retaining participants in real-world studies that collect primary data are challenging. This article illustrates these challenges using a post-authorization safety study (PASS) to assess adverse events (AEs) experienced with fentanyl buccal tablet (FBT) over 3 months of treatment. METHODS: This was an observational, prospective, multicenter study in France conducted over 1 year. The study employed primary data collection in FBT-treated patients and their treating physicians via a site qualification questionnaire and patient log completed by physicians and a questionnaire and pain diary completed by patients. Strategies to increase participation included reminders, newsletters, frequent follow-up telephone calls, and reducing the extent of data collected. RESULTS: Of the 1118 physicians contacted who returned the participation form or responded to a telephone call, only 128 expressed willingness to participate. Key reasons for non-participation were lack of interest (69.7%) and FBT not being used in practice by the contacted physician (25.1%). Overall, 224 patients were screened by 31 physicians, and 97 were enrolled. Key reasons for patient non-inclusion were unwillingness or inability to complete the patient AE diary or questionnaire (40.9% [52/127]) and patients' decision (33.9% [43/127]). CONCLUSIONS: Despite efforts to increase participation, enrollment in this study was low. Recruitment and retention methods are limited in their capacity to optimally execute a primary data collection in a PASS. For a PASS to provide reliable and valid information on medication use, involvement from health care agencies, regulators, and pharmaceutical companies is needed to establish their importance, drive study participation, and reduce patient withdrawal.
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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 | 0.282 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".