BiRD: A Strategy to Autonomously Supplement Clinical Practice Guidelines with Related Clinical Studies
Bibliographic record
Abstract
In this paper we introduce a framework to supplement and tag computerized CPG with related best-evidence automatically sourced from on-line medical literature repositories. The idea is to provide CPG users with additional published evidence pertaining to the different sections of a CPG of their interest. We present a web-enabled Best-evidence Retrieval and Delivery (BiRD) system that autonomously retrieves pertinent medical literature with respect to user-specified content from a GEM-encoded CPG. This is achieved via a multi-level literature search strategy that uses the actual CPG content to autonomously generate a search query. The featured search strategy firstly categorizes the search query towards a priori defined clinical query subjects and secondly filters out insignificant medical terms from the search query. The technical architecture comprises existing medical language processing tools and vocabularies, together with newly developed tools to automatically (a) generate optimum search queries; (b) retrieve medical articles from MEDLINE; and (c) embed the retrieved medical articles within XML-based CPG encoded according to the GEM formalism.
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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".