ASSESSING THE VALUE OF INNOVATIVE MEDICAL DEVICES AND DIAGNOSTICS: THE IMPORTANCE OF CLEAR AND RELEVANT CLAIMS OF BENEFIT
Bibliographic record
Abstract
OBJECTIVES: Large numbers of new medical devices and diagnostics are developed and health services need to identify which ones offer real advantages. The National Institute for Health and Care Excellence (NICE) has introduced a system for assessing technologies that are often notified by companies, based on claims made for their benefits to patients, the National Health Service, and the environment. METHODS: Detailed scrutiny of claims made for the benefits of products and the corresponding evidence, seeking associations between these and the selection of products for full evaluation to produce NICE guidance. RESULTS: Between 2009 and 2015 a NICE committee considered 169 technologies, of which it selected 74 (44 percent) for full evaluation, based on the claims of benefit and the evidence available. An average of 7.5 claims were made per technology; the total number did not influence selection but presence of studies supporting all the claims (p < .001) or any of the claims (p < .05) had a positive influence, as did claims for quicker patient recovery (p < .001). A greater number of studies to support the claims made selection more likely (p < .001), as did cohort studies (p < .05) and surveys (p < .05) but, unexpectedly, not randomized trials. The Medical Device Directive class had no influence. CONCLUSIONS: This study presents categories of claims that may be useful to those developing new products and to others engaged in health technology assessment. It illustrates the importance of relevant evidence and of having a clear vision of the place of new products in care pathways from an early stage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.323 | 0.681 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.012 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".