Developing an Instrumentation Package to Measure Noise and Vibration in Neonatal Patient Transport
Bibliographic record
Abstract
Inter-facility transport is often necessary for patients requiring specialized medical care. In the case of neonatal patients, specialized transport systems are used to ensure continuous care during transport. Concerns relating to physical stressors to which the patients are exposed during transport has motivated our ongoing study on evaluating and mitigating vibration and acoustic noise in these systems. Regardless of the various testing configurations, whether they be full-scale vehicle tests or laboratory shaker tests, reliable and consistent data collection practices are desired. An instrumentation package, designed to measure noise and vibration of the transport system, is currently in development to ultimately improve the safety of patients during transport. This system is intended to promote standard data collection practices and ultimately the aggregation of findings from independent studies into a single database. Requirements for the system have been defined, and early stages of sensor selection and prototyping are underway. The current configuration of sensors can successfully collect acceleration, angular rate, and sound level data. Additional sensors can be integrated into the system, to allow for multiple locations of interest to be studied simultaneously. Future work will improve the efficiency of the data collection script, the automation of data processing, and the physical implementation of the system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".