Integrating Protocol-driven Decision Support within E-Referral System: Supporting Primary Care Practitioners for Spinal Care Consultation and Triaging
Bibliographic record
Abstract
Referrals to the Halifax Infirmary Neurosurgery Department are submitted with regards to spinal conditions with different degrees of complications. Although there exists a Spinal Condition Consultation Protocol to standardize spinal referrals, the information provided from referring physicians is frequently inadequate to accurately triage the patient's condition, partly due to missing diagnostic therapies. The Neurosurgery Department receives a high volume of referrals each year, which imposes a significant administrative workload on the staff.\nWe propose to develop a protocol-driven decision support system to: 1) Provide primary care physicians with timely access to condition specific consultation treatment protocols; and 2) Automate the referral assessment process to eliminate processing delays and administration burden. To this aim, we transformed the Consultation Protocol into a semantic knowledgebase. The decision support services are integrated within a standardized electronic referral system. We believe this system can significantly improve the referral process at the Neurosurgery Division.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".