The Effect of Speed and Road Type on Sound Pressure Level During Neonatal Patient Transport
Bibliographic record
Abstract
Neonatal Infants (neonates) requiring a higher level of care often require transportation via road ambulance to neonatal intensive care units. The trip in the ambulance exposes these infants to potentially harmful noise and vibration. The aim of this study was to categorize the sound level in and around the Neonatal Patient Transport System (NPTS) during transport in ambulances at different speeds and different road types. Data collection was performed in Ottawa, Canada. The road types categorized in the study include arterial roads, collector/major collector roads, local roads, lanes and highways. Additionally, discrete events including rail-road crossings, speed humps and potholes were also categorized. For comparison, the sound levels in the ambulance cabin and near the driver were also taken and analyzed. By categorizing the effects these factors have on the sound level experienced by the neonate, better route planning and sound mitigation strategies can be employed. Using the sound levels in the decibel A scale (dBA) for each road test, trendlines could be created showing the variation in sound levels with speed and road type. It is observed that the average sound level experienced by the neonate in the NPTS (IsoletteMic) when stationary is approximately 56 dBA. A steady increase in sound level corresponding to an increase in speed was observed. However, there was no substantial variation in the sound level experienced on different road types at similar speeds. The results suggest that noise exposure increases significantly with vehicle speed, is largely independent of road type, and contains elements that are representative of the Children’s Hospital of Eastern Ontario (CHEO) patient transfers. Building off that knowledge, it is be suggested that improved noise shielding and route planning should be employed to improve neonates’ safety during transport.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".