Bibliographic record
Abstract
Pressure measurement using a Pressure Sensitive Mat (PSM) enables a non-contact approach for monitoring patient vital signs such as respiration rate (RR) and heart rate (HR).Non-contact patient monitoring in Neonatal Intensive Care Units (NICU) can improve the quality of patient care and reduce patient discomfort.This thesis investigates the applicability of PSM for monitoring of neonatal patient respiration rate in the NICU.A clinical trial was conducted with the Children's Hospital of Eastern Ontario (CHEO) collecting PSM data from 15 patients in three different bed types.Gold standard RR and annotations of patient movement and interventions were collected simultaneously.Algorithms for estimating RR were developed by optimizing spatial downsampling, reference signal selection, enhancement, and combining.The RR estimation results were evaluated against gold standard estimates from a commercial patient monitor.The best RR estimation method worked well when the patient is on ventilator support achieving a mean absolute error (MAE) of 3.9 +/-6.6 bpm on clean data without patient movements or interventions.For this patient, 89.7% of RR estimates were within the clinically acceptable range of +/-10 bpm, while 78.4% fell within the more stringent limits of +/-5 bpm.When the RR estimation algorithm is evaluated on all patients, RR estimation performance is mixed.In particular, very low mass neonates and patients laying on multiple blankets presented challenges for RR estimation.Taken together, a 95% confidence interval (CI) for agreement between the estimated and gold standard RR was estimated to be [-44.8,51.9] bpm with a mean bias of 3.5 bpm, using a mixed effects limits of agreement (LoA)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".