Parameter definition for aerosol-generating medical procedures inhospitals
Bibliographic record
Abstract
The demand for high-fidelity, clinically relevant computer models for airborne disease spread during aerosol generating medical procedures (AGMPs) has drastically risen due to the COVID-19 pandemic.AGMPs are routinely performed in hospital rooms of various size, ventilation rate, equipment layout, procedure acuity, and many more clinically variable parameters that may significantly change the risk level of airborne disease spread.Several studies have demonstrated the importance of human thermal plumes (HTPs), human movement, and ventilation system design when predicting the distribution of aerosols in healthcare settings.Some of these studies also show the effectiveness of new preventative technologies.Comparing the relative influence of each parameter and preventative device between each study is difficult because each individual experiment/simulation takes place in a specific or relatively narrow range of hospital settings.By applying Design of Experiment (DOE) methodologies, the relative importance of each clinically variable parameter and new preventative technology can be systematically tested.However, to run effective DOE screening studies, ranges of each input parameter need to be quantified.The purpose of this study is to present descriptive statistics for each room type where AGMPs may occur.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".