Coronavirus PPE: a positive pressure hood assembled from ubiquitous, low-cost materials
Bibliographic record
Abstract
Abstract A positive pressure protective hood system was purposefully constructed only from materials commonly found worldwide, including bendable aluminum mesh, elastic head straps, velcro tape, a plastic sheet, a furnace filter and two computer central processing unit (CPU) cooling fans. The practical advantages of this system are that the materials are readily available in the inventories of most electronics and hardware outlets, ease of assembly (particularly if choosing to employ 3D printing for the fan enclosure and/or making several units at once with a defined workflow), and high probability of the materials being available in current or prospective personal protective equipment (PPE)-deplete regions. An experiment with identical fire detectors showed adequate inner isolation of the hood prototype from paper combustion particulates, which have a size range slightly smaller than putative coronavirus aerosols, for at least 90 seconds. The theoretical advantages of this system include significant reduction in healthcare provider exposure to coronavirus-containing respiratory fomites, respiratory droplets and aerosols (vs. traditional static masks and shields) during high risk procedures such as endotracheal intubation or routine care of an upright and coughing patient. Additionally, the assembly eliminates contact exposure to coronavirus fomites due to whole-head coverage from a hood 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".