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Record W3012790049 · doi:10.22374/jded.v3i1.32

Chronic Ocular Graft Versus Host Disease: An Update and Review

2020· article· en· W3012790049 on OpenAlexvenueno aff
Jane Bachman Groth, John Conto, Marcelo C. Pasquini

Bibliographic record

VenueJournal of Dry Eye Disease · 2020
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeibomian glandDiseaseComplicationIntensive care medicineConjunctivaGraft-versus-host diseaseQuality of life (healthcare)CorneaArtificial tearsTransplantationSurgeryDermatologyOphthalmologyPathologyEyelid

Abstract

fetched live from OpenAlex

An updated comprehensive literature review was completed of chronic ocular graft versus host disease (oGVHD) to identify current and future considerations as to the causes, diagnosis, and treatment of this complication after allogenic hematopoietic cell transplantation (HCT). Graft-versus-host disease involves multiple organ systems, including the eye, and is a leading cause of mortality and morbidity in these patients. This review consisted a comprehensive search of the PubMed, ClinicalTrials.gov and NIH.gov databases.
 oGVHD is a debilitating and potentially sight threatening condition. Commonly involved ocular structures include the cornea, conjunctiva, meibomian glands, eyelids, lacrimal gland and tear film. Identifying and treating the ocular complications at the early stages may improve final outcomes and quality of life in these patients. Aggressive lubrication, preservation of tear film and inflammation control, including minimizing surface scarring, are treatment goals. Co-management with HCT and other pertinent health care providers is critical for early diagnosis and to initiate prompt therapy to minimize the ocular damage. Stepped therapy, including the use of emerging systemic treatments can be useful in the management of oGVHD with stable visual function, quality of life and complication management as goals of treatment.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.519

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 head0.294
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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