INDUSTRY'S EXPERIENCES WITH THE SCIENTIFIC ADVICE OFFERED BY THE FEDERAL JOINT COMMITTEE WITHIN THE EARLY BENEFIT ASSESSMENT OF PHARMACEUTICALS IN GERMANY
Bibliographic record
Abstract
OBJECTIVES: Optional scientific advice (SA) for the early benefit assessment of pharmaceuticals is offered by the German decision maker, the Federal Joint Committee (FJC). The aim of this study was to elicit manufacturers' experiences with the SA procedures offered by the FJC to date. METHODS: A preliminary survey on a small sample size was conducted. Subsequently, a questionnaire comprising eight items, which was developed on the basis of that survey, was used. Data were analyzed using qualitative and quantitative approaches. RESULTS: The elicitation, including a sample of 25 percent of the completed advice, highlighted the following, regarding the process as well as to the content shortcomings of the SA procedures from an industrial perspective: inconsistencies, FJC's lack of expertise in conducting clinical trials, partially incomplete answers. and a low willingness of the FJC to engage in dialogue with industry were criticized. On the other hand, the majority of respondents expressed a positive attitude concerning unambiguousness, completeness, traceability, discussion atmosphere, and the protocol of the advice. Early SA, before pivotal trials start, showed a significantly higher completeness compared with late SA with respect to endpoints and study duration. Within 4 years the quality of FJC's propositions on some topics improved significantly. CONCLUSIONS: Only a few statistically significant differences were detectable between early versus late SA. A positive trend in industry's perception of the SA can be observed over time. A more active involvement of additional stakeholders and the incorporation of procedural elements from other healthcare systems could improve the quality of the SA offered by the FJC.
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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 | 0.061 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".