A method for physiological data transmission and archiving to support the service of critical care using DICOM and HL7
Bibliographic record
Abstract
An increasing amount of physiological monitoring data is displayed on medical devices around the world every day. By and large, much of this data is lost beyond hand written annotations. Opportunities exist to utilize this data for improved care of those patients within the NICU and for clinical research. The service oriented architecture paradigm offers a way of thinking of critical care through the provision of services of critical care provided by clinicians where patients may be located within or outside their intensive care unit. A major inhibitor to this becoming reality is the lack of a standard for the representation of physiological data as HL7, for example, does not include definitions for time series data. This research proposes a method to represent, transmit and archive physiological data using DICOM and HL7. To enable this, a DICOM file writer and viewer for the physiological time-series data is proposed to specifically enable the storage requirement for these data. This research is then tested within the context of Neonatal Intensive Care.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".