Decision Support Technology in Knowledge Translation
Bibliographic record
Abstract
Information technologies, and specifically clinical decision support systems (CDSSs), are tools that can support the process of knowledge translation in the delivery of emergency department (ED) care. It is essential that during the implementation process, careful consideration be given to the workflow and culture of the ED environment where the system is to be utilized. Despite significant literature addressing factors contributing to successful deployment of these systems, the process is frequently problematic. Careful research and analysis are essential to evaluate the impact of the CDSS on the delivery of ED care, its influence on the health care providers, and the impact of the CDSS on clinical decision-making processes and information behaviors. The logistical and educational implications of CDSSs in the ED must also be considered. The specialty of emergency medicine must actively collaborate with other stakeholders in the design, implementation, and evaluation of CDSSs that will be utilized during the delivery of care to our patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".