56: EVALUATION OF SCIENTIFIC OUTPUTS OF HEALTH TECHNOLOGY ASSESSMENTS IN PUBMED
Bibliographic record
Abstract
Background and aims: Health technology assessments (HTA's) have been known as a scientific approach for improving of patient outcomes also efficacy promoting of health systems. Therefore, in this study, scientific publications of HTAs in PubMed have been assessed. Methods: In this descriptive-scientometrics research, all HTA studies were searched in PubMed in August 2016. For this purpose, special keywords were searched in title, abstract and Mesh subject headings without any date limitation. Then, 2881 retrieved documents were analyzed. Results: According to the findings, the first document in the field of HTA published in 1978 and then in 1983. Also, the most of documents published in 2015, 2014, 2016 consequently. In addition, the most frequency of affiliations belonged to VANCOUVER BC' TORONTO ON and MONTREAL QC. Furthermore, SIEBERT U' DRUMMOND M and HAILEY D were the most active authors. Moreover, three journals include INTERNATIONAL JOURNAL OF TECHNOLOGY ASSESSMENT IN HEALTH CARE' HEALTH TECHNOLOGY ASSESSMENT (WINCHESTER, ENGLAND) and VALUE IN HEALTH: THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS with 32% of publications had the highest portion in this area and English language with 90% were in the first rank, then, German, French and Spanish were in the next ranks. Finally, the majority of publications were Journal Article and then RESEARCH SUPPORT, NON-U.S. GOV'T and REVIEW allocated next places. Conclusion: Health technology assessments are important tools in order to help for policy making, programming and technology management in health care services. Hence, evaluation of publication in this domain can reflect published research trend and lead to conduct ongoing studies to provide studies Effectiveness toward society requirements and government's policies.
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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 | 0.118 | 0.431 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.107 | 0.089 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".