Artifact detection and data reconciliation in multivariate ventilatory variables measured during anesthesia: A case study
Bibliographic record
Abstract
A ventilation system is usually connected to an anesthetized patient during surgery to replace or support normal respiratory function. Clinician experts monitor the concentration, flow and pressure of the various gases in the airway to maintain adequate ventilation. However, environmental disturbances often perturb the readings of these variables and cause them to deviate far from the true levels, resulting in a biased evaluation of a patient's ventilatory status. Most of the previously proposed signal estimation methods, however, have only utilised the difference of the dynamic characteristics between artifacts and the true physiological variations in each individual variable, without considering the interrelationship between these signals. This has resulted in suboptimal signal estimation. In this paper, we propose the use of the procedure of gross error detection and data reconciliation commonly used in process control, to detect highly cross-correlated artifacts in the ventilation circuit and reconcile the uncontaminated signal measurements. A case study demonstrates that the proposed method has great potential for improving the reliability of ventilatory signals.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.001 |
| 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 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".