OP49 A Systematic Review Of The Activities Of Early Advice, Early Dialogue, Scientific Advice By HTA Doers
Bibliographic record
Abstract
Introduction There is a range of activities that health technology assessment (HTA) doers have started to improve the process of generation of required evidence for new technologies, and the alignment of regulatory and reimbursement processes that retard the access to patients to them. Different organizations call those processes early advice, early dialogue, or scientific advice to those activities. Methods We performed a systematic review of the activities named scientific advice (SA), early advice (EA) and early dialogue (ED). Major databases and HTA organizations were explored. The protocol and search strategy were published in PROSPERO. The selection of final articles and documents was done in pairs, and when discrepancies were found a third person resolved with the consensus of the others. A matrix was used to define the commonalities and differences of the described processes. Results We initially retrieved 949 documents, after the analysis of duplications and the full text reading of the selected ones, we finally selected 39 documents and described: the type of technologies, the process, the stakeholders, the duration, the costs, and the impact. Big HTA agencies such as the Canadian Agency for Drugs and Technologies in Health (CADTH) or the National Institute for Health and Care Excellence (NICE) included EA or SA among their portfolio of activities as well as networks (European Network for HTA (EUnetHTA) or smaller agencies such as HTA Wales or Basque Office for HTA (Osteba) among others. The type of activity, the process, duration, purpose and costs differ among HTA doers. Conclusions There is a need to define what we meant when we are talking about SA, ED, and EA. In fact, regulators used the same processes with different purposes. Our systematic review and the lessons learnt from the European-funded SAFENMEDTECH project will propose a detailed framework that can be useful to better understanding the needs of each of the involved parties and how to make the processes involved more efficient.
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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 | 0.091 | 0.333 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.039 | 0.037 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".