MétaCan
Menu
Back to cohort
Record W3123313124 · doi:10.12927/hcq.2020.26395

Decontaminating N95 Respirators for Reuse in a Hospital Setting

2021· article· en· W3123313124 on OpenAlex
Tabitha Chiu, Jennifer Jeon, Betty-Jo Edgell, Garry Bassi

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHealthcare Quarterly · 2021
Typearticle
Languageen
FieldMedicine
TopicHealthcare and Environmental Waste Management
Canadian institutionsLunenfeld-Tanenbaum Research Institute
Fundersnot available
KeywordsRespiratorReuseMedical emergencyBest practiceBusinessMedicineOperations managementWaste managementEngineeringManagement

Abstract

fetched live from OpenAlex

With the global outbreak of the COVID-19 pandemic, hospitals in Canada and around the world have been forced to consider conservation strategies to ensure continued availability of personal protective equipment (PPE) for healthcare providers. To mitigate critical PPE shortages, Sinai Health System (Sinai Health), a large academic healthcare institution in Canada, has developed and operationalized a standard operating procedure for the collection, decontamination and reuse of N95 respirators and other single-use PPE using a vaporized hydrogen peroxide decontamination method. Sinai Health has incorporated stringent quality assurance steps to ensure that the N95 respirators are successfully decontaminated without deformation and are safe to use.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.312
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it