Evidence‐based decision making and asthma in the internet age: the tools of the trade
Bibliographic record
Abstract
At the dawn of the Information Age, the practice of evidence-based decision making (EBDM) is still hindered by many important barriers related to the decision makers, to the evidence per se or to the health system. Some of these barriers, particularly those related to the distillation, dissemination and packaging of research evidence, could be overcome by recent and ongoing developments in portable/wearable computers, internet appliances, multimedia and wireless broadband internet traffic. This article describes specific EBDM-related tools, with emphasis on internet-enabled "how to" books; and tools to improve the quality of reporting research, to formulate questions; to search for evidence; to access journals, systematic reviews and guidelines; to interact with organizations promoting EBDM; and to tailor evidence to individual cases. However, thinking that all barriers to the practice of EBDM could be solved by fancy information technology is naïve. Barriers related to the generation, interpretation, integration and use of the evidence demand more complex and perhaps unfeasible solutions, as overcoming them will require substantial changes in the structure of the health system, in the politics of science and in the way in which humans think and behave.
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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 | 0.021 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".