Bibliographic record
Abstract
HTA is an interdisciplinary assessment of evidence and knowledge about the intended and unintended effects of using a health technology. Patients with rare diseases have valuable knowledge about the illness in the real-life setting, but too often their views are seen as anecdotal or biased. So, more needs to be done to elicit patients’ perspectives to add value to HTA through effective participation of patients throughout the HTA process and collection of evidence about patients’ perspectives through robust qualitative research. Traditionally HTA has been a broad assessment to move evidence into practice, but in recent years a more limited view of clinical and cost effectiveness has been the focus. For HTAs in rare diseases, this is not enough. Consideration of ethical, organizational and social issues are vital and here patients’ perspectives could be particularly valuable to bring a real-life understanding of the potential impact of the health technology. As countries around the world put more emphasis on creating clear plans to manage rare diseases, we need to ensure that all stakeholders work together to ensure that HTAs are being used flexibly to ensure that there is equity of access to therapies for rare diseases that provide real added value.
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 | 0.090 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.021 | 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".