Conceptual Framework for A Perinatal Decision Support System using a Knowledge-Based Approach
Bibliographic record
Abstract
This paper discusses the development of a knowledge based perinatal clinical decision support system (CDSS) to predict preterm labour. It consists of a knowledge-base, a workflow engine, and a mechanism to communicate results. The knowledge base contains rules or associations related to the desired predictions; the workflow engine combines the rules in the knowledge base with the patient data; and the communication mechanism allows entry of the patient data into the system, and output of results in the form of notifications, alerts or emails. This system will help physicians to inform families and to initiate preventative care, monitoring, and treatment. The final form of the CDSS is to be integrated to an electronic medical record (EMR) and thus allow for auto-population of the patient data into appropriate fields. A web-based collaborative platform that meets the legal and regulatory accreditation standards will be used to deliver information relevant to clinical users.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".