The clinical relevance and newsworthiness of NIHR HTA-funded research: a cohort study
Bibliographic record
Abstract
OBJECTIVE: To assess the clinical relevance and newsworthiness of the UK National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme funded reports. STUDY DESIGN: Retrospective cohort study. SETTING: The cohort included 311 NIHR HTA Programme funded reports publishing in HTA in the period 1 January 2007-31 December 2012. The McMaster Online Rating of Evidence (MORE) system independently identified the clinical relevance and newsworthiness of NIHR HTA publications and non-NIHR HTA publications. The MORE system involves over 4000 physicians rating publications on a scale of relevance (the extent to which articles are relevant to practice) and a scale of newsworthiness (the extent to which articles contain news or something clinicians are unlikely to know). MAIN OUTCOME MEASURES: The proportion of reports published in HTA meeting MORE inclusion criteria and mean average relevance and newsworthiness ratings were calculated and compared with publications from the same studies publishing outside HTA and non-NIHR HTA funded publications. RESULTS: 286/311 (92.0%) of NIHR HTA reports were assessed by MORE, of which 192 (67.1%) passed MORE criteria. The average clinical relevance rating for NIHR HTA reports was 5.48, statistically higher than the 5.32 rating for non-NIHR HTA publications (mean difference=0.16, 95% CI 0.04 to 0.29, p=0.01). Average newsworthiness ratings were similar between NIHR HTA reports and non-NIHR HTA publications (4.75 and 4.70, respectively; mean difference=0.05, 95% CI -0.18 to 0.07, p=0.402). NIHR HTA-funded original research reports were statistically higher for newsworthiness than reviews (5.05 compared with 4.64) (mean difference=0.41, 95% CI 0.18 to 0.64, p=0.001). CONCLUSIONS: Funding research of clinical relevance is important in maximising the value of research investment. The NIHR HTA Programme is successful in funding projects that generate outputs of clinical relevance.
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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 | 0.010 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".