The Information Infrastructure that Supports Evidence-Based Veterinary Medicine: A Comparison with Human Medicine
Bibliographic record
Abstract
In human medicine, the information infrastructure that supports the knowledge translation processes of exchange, synthesis, dissemination, and application of the best clinical intervention research has developed significantly in the past 15 years, facilitating the uptake of research evidence by clinicians as well as the practice of evidence-based medicine. Seven of the key elements of this improved information infrastructure are clinical trial registries, research reporting standards, systematic reviews, organizations that support the production of systematic reviews, the indexing of clinical intervention research in MEDLINE, clinical search filters for MEDLINE, and point-of-care decision support information resources. The objective of this paper is to describe why these elements are important for evidence-based medicine, the key developments and issues related to these seven information infrastructure elements in human medicine, how these 7 elements compare with the corresponding infrastructure elements in veterinary medicine, and how all of these factors affect the translation of clinical intervention research into clinical practice. A focused search of the Ovid MEDLINE database was conducted for English language journal literature published between 2000 and 2010. Two bibliographies were consulted and selected national and international Web sites were searched using Google. The literature reviewed indicates that the information infrastructure supporting evidence-based veterinary medicine practice in all of the 7 elements reviewed is significantly underdeveloped in relation to the corresponding information infrastructure in human medicine. This lack of development creates barriers to the timely translation of veterinary medicine research into clinical practice and also to the conduct of both primary clinical intervention research and synthesis research.
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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 | 0.169 | 0.366 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.028 | 0.036 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.026 | 0.029 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".