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Record W3083772003 · doi:10.1097/mcg.0000000000001411

Personal Protective Equipment for Endoscopy in Low-Resource Settings During the COVID-19 Pandemic

2020· review· en· W3083772003 on OpenAlexaff
Desmond Leddin, David Armstrong, Raja Affendi Raja Ali, Alan Barkun, Amna Subhan Butt, Ye Chen, Harshit S. Khara, Yeong Yeh Lee, Wai K. Leung, Finlay Macrae, Govind Makharia, Reza Malekzadeh, Elias Makhoul, Anahita Sadeghı, Jean‐Christophe Saurin, Mark Topazian, S R Thomson, Andrew Veitch, Kaichun Wu

Bibliographic record

VenueJournal of Clinical Gastroenterology · 2020
Typereview
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcGill UniversityMcMaster UniversityDalhousie University
Fundersnot available
KeywordsPersonal protective equipmentMedicinePandemicCoronavirus disease 2019 (COVID-19)Health careLimitingEconomic shortageMedical emergencyResource (disambiguation)Risk analysis (engineering)EndoscopyFlexibility (engineering)SurgeryDiseasePathologyComputer science

Abstract

fetched live from OpenAlex

Performance of endoscopic procedures is associated with a risk of infection from COVID-19. This risk can be reduced by the use of personal protective equipment (PPE). However, shortage of PPE has emerged as an important issue in managing the pandemic in both traditionally high and low-resource areas. A group of clinicians and researchers from thirteen countries representing low, middle, and high-income areas has developed recommendations for optimal utilization of PPE before, during, and after gastrointestinal endoscopy with particular reference to low-resource situations. We determined that there is limited flexibility with regard to the utilization of PPE between ideal and low-resource settings. Some compromises are possible, especially with regard to PPE use, during endoscopic procedures. We have, therefore, also stressed the need to prevent transmission of COVID-19 by measures other than PPE and to conserve PPE by reduction of patient volume, limiting procedures to urgent or emergent, and reducing the number of staff and trainees involved in procedures. This guidance aims to optimize utilization of PPE and protection of health care providers.

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 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)0.005
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.214
GPT teacher head0.515
Teacher spread0.301 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2020
Admission routes1
Has abstractyes

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