Augmenting GEM-encoded clinical practice guidelines with relevant best evidence autonomously retrieved from MEDLINE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Clinical practice guidelines (CPG) are instrumental in standardizing the quality and delivery of care across different practitioners, departments and institutions. Health practitioners will use current best evidence to validate or supplement their understanding of CPG. This study investigates the potential of supplementing computerized CPG with relevant best evidence sourced from reliable medical literature repositories. A web-enabled Best-evidence Retrieval and Delivery (BiRD) system facilitates autonomous retrieval of pertinent medical literature with respect to user-specified content from a GEM-encoded CPG. A multilevel literature search strategy categorizes the search towards predefined clinical query intentions, and subsequently filters insignificant medical terms. The resultant is a highly focused medical literature search query that is objectively derived from CPG content. The technical architecture comprises existing medical language processing tools and vocabularies, together with newly developed tools to automatically generate optimum search queries, retrieve medical articles from MEDLINE, and embed the articles within XML-based CPG.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it