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Record W3118619712 · doi:10.1093/ofid/ofaa439.685

492. Canadian consensus of COVID-19 policy and management aspects

2020· article· en· W3118619712 on OpenAlexaffabout
Kara K. Tsang, Dominik Mertz, Zain Chagla, Fiona Smaill, Sarah Khan

Bibliographic record

VenueOpen Forum Infectious Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicinePersonal protective equipmentInfection controlContext (archaeology)Family medicineHealth careCoronavirus disease 2019 (COVID-19)OutbreakInfectious disease (medical specialty)DiseaseIntensive care medicinePathology

Abstract

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Abstract Background As evidence rapidly changes, a need for consensus in hospital policy and management aspects of COVID-19 patient care are needed. This study describes areas where consensus exists and is needed in infection control, and occupational health policy. Methods An online survey was sent to the membership of the Association of Medical Microbiology and Infectious Disease (n~700). The survey included questions about COVID-19 patient and outbreak management, personal protective equipment (PPE), and occupational health considerations. Results Our preliminary results (n=24) were from infectious disease MD/NP or infection control medical directors. All respondents agreed treatment of COVID-19 patients should only occur in the context of a clinical trial. Of 18 centers with neonatal populations, the majority (64.2%) did not have any neonatal specific treatment guidelines. Well-babies born to COVID-19 positive moms, are all being tested (10 of 10 respondents). Variation in practice on when to remove a patient from additional precautions and potential aerosol generating medical procedures (Table 1, 2). Universal masking is in place for all clinical staff (100%), non-clinical staff (70.8%), essential visitors or patient caregivers (70.8%), and universal eye protection is in place for clinical staff (93.3%), but there was a lack of consensus in PPE conservation strategies (Table 3). Most staff do not use neck PPE (68.2%), however there was comments of it being requested by anesthesiologists at 2 sites (Table 2). Healthcare trainees or workers in these groups were restricted from caring for COVID-19 patients; Age >65 years (54.5%) and immunocompromised status (54.5%). COVID-19 positive staff can return to work 14 days after symptom onset (84.2%). Table 1. Areas of COVID-19 management lacking consensus. Not all respondents answered every question. The percentage in brackets was calculated with the number of respondents per question as the denominator. Table 2. Procedures considered as aerosol generating medical procedures (AGMPs). Respondents (n=24) were allowed to select more than one option. Table 3. Personal protective equipment (PPE) conservation strategies (n=24). Not all respondents answered every question. The percentage in brackets was calculated with the number of respondents per question as the denominator. NA corresponds to the question not asked in the survey. Conclusion Across Canada, while there are areas of consensus in outbreak definitions, universal masking of clinical staff. There is significant variation in practice with respect to discontinuing additional precautions or outbreak measures, asymptomatic testing, AGMP definitions, PPE conservation strategies including reprocessing. As evidence evolves, national infection control guidelines will be important to improve standardization of practice and optimize patient care and staff safety. Disclosures All Authors: No reported disclosures

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 imitation

Not 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.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.148
Threshold uncertainty score0.988

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.076
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0110.004
Scholarly communication0.0070.003
Open science0.0040.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0200.002

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.

Opus teacher head0.051
GPT teacher head0.383
Teacher spread0.333 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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