Almost half of references in reports on new and emerging nondrug health technologies are grey literature
Bibliographic record
Abstract
OBJECTIVE: The research investigated how frequently grey literature is used in reports on new and emerging nondrug health technologies, which sources are most cited, and how grey literature searching is reported. METHODS: A retrospective review of references cited in horizon scanning reports on nondrug health technologies-including medical devices, laboratory tests, and procedures-was conducted. A quasi-random sample of up to three reports per agency was selected from a compilation of reports published in 2014 by international horizon scanning services and health organizations. RESULTS: Twenty-two reports from 8 agencies were included in the analysis. On average, 47% (288/617) of references listed in the bibliographies of the horizon scanning reports were grey literature. The most frequently cited type of grey literature was information from manufacturers (30% of all grey literature references), regulatory agencies (10%), clinical trial registries (9%), and other horizon scans or evidence synthesis reports (9%). The US Food and Drug Administration (FDA) and ClincalTrials.gov were the most frequently cited specific sources, constituting 7% and 8% of grey literature references, respectively. Over two-thirds (15/22) of the analyzed reports provided some details on search methodology; all 15 of these reported searching some grey literature. CONCLUSIONS: In this sample, grey literature represented almost half of the references cited in reports on new and emerging nondrug health technologies. Of these grey literature references, almost half came from three sources: the manufacturers, ClincalTrials.gov, and the FDA. There was wide variation in the other sources cited. Literature search methodology was often insufficiently reported for analysis.
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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 | 0.081 | 0.418 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.082 | 0.094 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".