Informatics and Knowledge Translation
Bibliographic record
Abstract
To ensure that the benefits of knowledge translation synthesis are accessible to care providers at the point of decision-making, fast, efficient, usable clinical information systems are required. Medical informatics appears to hold the greatest promise to be able to create systems with the necessary capacity and functionality. Emergency medicine needs to be actively engaged at all levels of the process. This includes driving the development and filtering of emergency-specific synopses and summaries. It requires advocating for hardware and software that suit the needs of the emergency department environment. It is increasingly important to educate and participate on committees with funders and policy-makers to ensure they support this growing evolution. To determine the outcome of these initiatives, careful evaluation is required to inform the discussion. End-users need to be actively involved in the development and usability testing of clinical information retrieval technology and clinical decision-support systems and make certain relevant best evidence is readily accessible and formatted to meet the needs of the working emergency physician. The integration of knowledge translation into clinical practice, and the impact of delivering electronic clinical decision-support, requires methodologically sound studies to confirm or refute its benefits and guide future development of medical informatics.
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 | 0.036 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".