Analysing the Performance of a Real-Time Healthcare 4.0 System using Shared Frailty Time to Event Models
Bibliographic record
Abstract
This paper introduces the real-time Healthcare 4.0 system, the VILIAlert system and a new approach that we propose for the robust assessment of it's performance. The VILIAlert system alerts clinicians when a patient's tidal volume value rises above the clinically accepted level of 8 ml/kg as beyond this point (> 8 ml/kg), a patient is considered high risk of permanent damage to their lungs. In order to ensure success with the VILIAlert system, the ideal scenario is to ensure that as soon as patients in the Intensive Care Unit experience tidal volume values beyond the 8 ml/kg level, a clinical intervention can be carried out so to minimise the risk of patients ever having permanent damage. The approach has been implemented in the Intensive Care Unit at the Royal Victoria Hospital Belfast, Northern Ireland demonstrating the potential for such an approach to be used across all hospitals in the region.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".