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Record W4385899526 · doi:10.3389/fmed.2023.1210293

Global impact of COVID-19 on corneal donor tissue harvesting and corneal transplantation

2023· review· en· W4385899526 on OpenAlexaff
Morteza Mousavi, Nicolás Kahuam-López, Alfonso Iovieno, Sonia N. Yeung

Bibliographic record

VenueFrontiers in Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCorneal transplantationMedicinePandemicCoronavirus disease 2019 (COVID-19)OphthalmologyTransplantationEtiologyCorneaCorneal TransplantSurgeryDiseasePathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Introduction: The purpose of this review is to consolidate and examine the available literature on the coronavirus disease 2019 pandemic and its effect on corneal transplantation and eye banking. Methods: A primary literature search was conducted using the PubMed (Medline) database with keywords and MeSH terms such as "corneal transplantation," "eye banks," "keratoplasty" and then were combined with COVID-19. Relevant articles through September 2022 were assessed and 25 articles were included in this review. Results: Donor tissue volumes declined globally during lockdown periods due to a lower number of referrals and tighter tissue screening guidelines. Rates of elective surgeries decreased in the lockdown period compared to respective periods in previous years. However, changes in rates of emergency procedures were not uniform across different regions. Moreover, rates of different elective corneal grafts [i.e., penetrating keratoplasty (PK), endothelial keratoplasty (EK), or anterior lamellar keratoplasty (ALK)] were affected differently with the pattern of change being dependent on region-specific factors. Conclusion: Both donor tissue volumes and rates of corneal transplant procedures were affected by lockdown restrictions. The underlying etiology of these changes differed by region. Examining the range of impact across many countries as well as the contributing factors involved will provide guidance for future global pandemics.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.070
GPT teacher head0.408
Teacher spread0.338 · 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 designOther design
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

Citations8
Published2023
Admission routes1
Has abstractyes

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