A framework for secure integration of distributed point-of-care testing results into Electronic Medical Records
Bibliographic record
Abstract
Point-of-care testing (POCT) data is inherently distributed in nature and, unfortunately, seldom make it to the patient's Electronic Medical Records due to privacy issues, lack of standards, lack of connectivity, and absence of industry standard protocols. A Health Level 7 (HL7) compliant, secure data communication infrastructure has been developed to replicate communication patterns commonly found in healthcare environments. This infrastructure is used to demonstrate how POCT results collected using mobile devices can be securely integrated with existing medical information systems. Such integration improves patient's care by making the results immediately available to the health care team, allowing for well-informed and effective clinical decisions.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".