1426. Increased Visits to Respiratory Protection Webpages during COVID-19
Bibliographic record
Abstract
Abstract Background COVID-19 transmission is thought to occur mainly via respiratory droplets produced when an infected person coughs or sneezes. Respiratory protection devices, when properly fitted and used, can prevent this type of illness transmission. Methods The Centers for Disease Control and Prevention’s National Personal Protective Technology Laboratory (NPPTL) within the National Institute of Occupational Safety and Health (NIOSH) is responsible for the certification and approval of respirators for use in occupational settings. NPPTL maintains webpages with information on respiratory protection devices. We monitored the number of webpage views for several NPPTL webpages from January 1, 2020 to May 8, 2020. The number of webpage visits was then compared to significant events during the COVID-19 outbreak as well as the previous year’s webpage visits. Results The landing page for NIOSH-approved N95 respirators received the most visits. This page received a total of 1,637,250 webpage visits with a peak of 63,715 on February 26, 2020, a 38,989% increase from the average daily page visits for that same month in 2019. This occurred on the same day as the White House gave its first televised briefing on COVID-19. The page providing general information on filtering facepiece respirators, including N95 respirators received 834,186 webpage visits with a peak of 20,520 on April 3, 2020, a 13,659% increase from the average daily webpage visits for that same month in 2019. This occurred on the same date as new CDC recommendations were provided for using cloth face masks in public places. The counterfeit respirator page maintained a steady, but small number of webpage visits, but increased from 4,261 on March 27, 2020 to 68,145 on March 29, 2020. This occurred as the first reports of fraudulent respirators were being published in the media. It then fell to 18,562 on March 30, 2020 and 13,093 on March 31, 2020. While most webpage visits were from the United States, visits from China, and Canada were higher than previous years. Conclusion During COVID-19 there was a large increase in the number of webpage visits for respiratory protection information which coincided with major events and media reports. Accurate, accessible respiratory protection information is an important resource during public health emergencies. Disclosures All Authors: No reported disclosures
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".