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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".