Standards for physiological data transmission and archiving for the support of the service of critical care
Bibliographic record
Abstract
Physiological data is monitored and displayed on medical devices around the world every day, and the volume of this data is steadily increasing and newer monitoring devices enter the clinical setting. However, the vast majority of this data is lost since it is most often displayed once as it is recorded, perhaps replayed one or more times while it exists in the device's volatile memory. What little data that is permanently recorded is most commonly saved through hand written annotations, in paper records and in some limited samples stored on hospital clinical information systems. Meanwhile, current methods of data analysis provide opportunities to utilize this data for improved care of these same critical care patients. A major inhibitor to this becoming reality is the lack of standards for the representation, transmission and storage of physiological data. HL7, for example, does not include definitions for time series data. Research into the use of these data will soon be reaching the clinical setting and the need for such standards to be defined is becoming urgent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.077 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.014 |
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".