VALUE OF DATABASES OTHER THAN MEDLINE FOR RAPID HEALTH TECHNOLOGY ASSESSMENTS
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to explore the degree to which databases other than MEDLINE contribute studies relevant for inclusion in rapid health technology assessments (HTA). METHODS: We determined the extent to which the clinical, economic, and social studies included in twenty-one full and four rapid HTAs published by three Canadian HTA agencies from 2007 to 2012 were indexed in MEDLINE. Other electronic databases, including EMBASE, were then searched, in sequence, to assess whether or not they indexed studies not found in MEDLINE. Assessment topics ranged from purely clinical (e.g., drug-eluting stents) to those with broader social implications (e.g., spousal violence). RESULTS: MEDLINE contributed the majority of studies in all but two HTA reports, indexing a mean of 89.6 percent of clinical studies across all HTAs, and 88.3 percent of all clinical, economic, and social studies in twenty-four of twenty-five HTAs. While EMBASE contributed unique studies to twenty-two of twenty-five HTAs, three rapid HTAs did not include any EMBASE studies. In some instances, PsycINFO and CINAHL contributed as many, if not more, non-MEDLINE studies than EMBASE. CONCLUSIONS: Our findings highlight the importance of assessing the topic-specific relative value of including EMBASE, or more specialized databases, in HTA search protocols. Although MEDLINE continues to be a key resource for HTAs, the time and resource limitations inherent in the production of rapid HTAs require that researchers carefully consider the value and limitations of other information sources to identify relevant studies.
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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 | 0.540 | 0.855 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.191 | 0.171 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.035 | 0.028 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".