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Record W4220818370 · doi:10.1080/19382014.2022.2047571

Impacts of the COVID-19 pandemic on a human research islet program

2022· article· en· W4220818370 on OpenAlexafffundabout
Tina J. Dafoe, Theodore dos Santos, Aliya F Spigelman, James Lyon, Nancy Smith, Austin Bautista, Patrick E. MacDonald, Jocelyn E. Manning Fox

Bibliographic record

VenueIslets · 2022
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsPandemicCoronavirus disease 2019 (COVID-19)IsletDistribution (mathematics)BiologyMedicineDiseaseDiabetes mellitusInfectious disease (medical specialty)Internal medicineEndocrinology

Abstract

fetched live from OpenAlex

Designated a pandemic in March 2020, the spread of severe acute respiratory syndrome virus 2 (SARS-CoV2), the virus responsible for coronavirus disease 2019 (COVID-19), led to new guidelines and restrictions being implemented for individuals, businesses, and societies in efforts to limit the impacts of COVID-19 on personal health and healthcare systems. Here we report the impacts of the COVID-19 pandemic on pancreas processing and islet isolation/distribution outcomes at the Alberta Diabetes Institute IsletCore, a facility specializing in the processing and distribution of human pancreatic islets for research. While the number of organs processed was significantly reduced, organ quality and the function of cellular outputs were minimally impacted during the pandemic when compared to an equivalent period immediately prior. Despite the maintained quality of isolated islets, feedback from recipient groups was more negative. Our findings suggest this is likely due to disrupted distribution which led to increased transit times to recipient labs, particularly those overseas. Thus, to improve overall outcomes in a climate of limited research islet supply, prioritization of tissue recipients based on likely tissue transit times may be needed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.465
Teacher spread0.251 · 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
GenreEmpirical

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

Citations4
Published2022
Admission routes3
Has abstractyes

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