Bibliographic record
Abstract
Canada's first standardized Neonatal Patient Transport System (NPTS) has recently been introduced in Ontario.Vibrations experienced by neonatal patients during transport in the NPTS have been a source of concern, since previous studies have suggested that such vibrations could increase morbidity and mortality rates for neonates.This thesis develops an experiment protocol to quantify vibration exposure for neonates both on-road and in a standardized test environment.A road test mimicking actual transport was carried in a ground ambulance with the NPTS.Experiments in the standardized test environment have demonstrated the first successful installation and validation of an entire NPTS atop an industrial shaker table.The shaker table was used to simulate the motion of the ground ambulance vehicle floor with acceptable fidelity, and to test different variables.On-road results show an amplification of vibrations at the neonate's head, relative to the floor, most notably at frequencies surrounding 9.5 Hz, and that neonates likely experience vibration levels exceeding the standards recommended for adults.Shaker table results demonstrate that the custom mattresses decreased the vibration when compared to the currently used Geo-Matrix™ mattress by 2-4 times, and the use of a head restraint harness showed a decrease in vibration when compared to the standard five-point harness by 1.7-3.3times.These findings suggest the use of one of the custom mattresses in combination with the head restraint harness during neonatal transport.In addition, an analysis of vibration propagation through the NTPS equipment stack provides constructive suggestions for future research into mitigation strategies. 5.2.3Data Preprocessing ........
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".