OP19 Are Compassionate Use Programmes Good Predictors of Clinical Benefit?
Bibliographic record
Abstract
Introduction: In cases of high unmet clinical need, patients can access drugs prior to marketing authorization (MA) and Health Technology Assessment (HTA) through compassionate use programmes (CUP) or special access pathways (SAP). In theory, accelerated access is beneficial for patients with few therapeutic alternatives. In practice, it remains unclear if early access products actually deliver meaningful clinical benefit. Methods: Seventy-five drug-indication pairs were identified that have proceeded through a CUP or SAP in one or more countries including Canada, Australia, France, Sweden, England, and Scotland. Data was collected from regulatory and HTA websites on length of CUP or SAP, time prior to MA, time prior to HTA decision, time between MA and HTA decision, French Transparency Commission added clinical benefit (ASMR), and HTA decision. Cohen kappa scores were calculated in order to assess inter-agency agreement. Results: Across the 75 drug-indication pairs, average time between CUP and marketing authorization was 243 days, and average time between MA and HTA decision was 252 days. No products were deemed to be of major added clinical benefit (ASMR I), only 2.7 percent of products had important added clinical benefit (ASMR II), 26.7 percent of products had moderate added clinical benefit (ASMR III), 40.0 percent of products had minor added clinical benefit (ASMR IV), and 22.7 percent of products had no added clinical benefit (ASMR V). There is little inter-agency agreement in HTA recommendations for products that have proceeded through a CUP. The highest amount of agreement was seen between Canada and Scotland (k = 0.24). Conclusions: Preliminary results suggest that CUP and SAP products accelerate access, but often only provide only moderate or minor improvements in clinical benefit. Further, there is very little agreement across HTA agencies on the value of these products.
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".