Tau PET Imaging in Neurodegenerative Disorders
Bibliographic record
Abstract
Decontamination of N95 respirators has become critical to alleviate PPE shortages for healthcare workers in the current COVID-19 emergency. The factors that are considered for the effective reuse of these masks are the fit, filter efficiency and decontamination/disinfection level both for SARS-CoV-2, which is the causative virus for COVID-19, and for other organisms of concern in the hospital environment such as Staphylococcus aureus or Clostridium difficile. In its guidance entitled ‘Recommendations for Sponsors Requesting EUAs for Decontamination and Bioburden Reduction Systems for Surgical Masks and Respirators During the Coronavirus Disease 2019 (COVID19) Public Health Emergency’ (May 2020)[1], the FDA recommends a 6-log10 reduction in either the most resistant bacterial spores for the system or in a mycobacterium species to authorize the use of a decontamination method of N95 respirators for single or multiple users. While the goal is primarily inactivation against SARS-CoV-2, testing of decontamination methods against the virus may not always be available. For decontamination methods considered for only single users, the recommendation is a 6-log10 reduction in the infective virus concentration of 3 non-enveloped viruses or in the concentration of two Gram (+) and two Gram (-) bacteria. Based on these recommendations, we explored the efficacy of vaporized H2O2 (VHP) treatment of N95 respirators against surrogate viruses covering a wide range of disinfection resistance for emergency decontamination and reuse to alleviate PPE shortages for healthcare workers in the COVID-19 emergency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".