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Record W4405928198 · doi:10.4103/pajo.pajo_90_24

Map-dot-fingerprint (epithelial basement membrane) corneal dystrophy: A clinicopathological study

2024· article· en· W4405928198 on OpenAlexaffabout
Emily Marcotte, Pedro Fraiha, Angela Fajardo, Devinder Cheema, Mahshad Darvish-Zargar, Miguel N. Burnier

Bibliographic record

VenueThe Pan-American Journal of Ophthalmology · 2024
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsBasement membraneCorneal dystrophyFingerprint (computing)OphthalmologyDystrophyCorneaMedicinePathologyBiologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Background: Map-dot-fingerprint (MDF) is a corneal epithelial dystrophy affecting the basement membrane that presents bilaterally or in an asymmetric manner. Clinically, it appears as opacities or fingerprint lines and histopathologically, microcystic structures, dot-like patterns, or basement membrane thickening are commonly described features. Materials and Methods: We conducted a retrospective study on 10 cases of MDF obtained from 9 patients between 2017 to 2018 from the MUHC-McGill Ocular Pathology & Translational Research Laboratory. Results: Following histopathological evaluation with hematoxylin and eosin, periodic acid–Schiff (PAS), and in one case Alcian Blue, a final diagnosis of MDF was reached in all cases. Conclusion: This case series aims to document MDFs histopathological characteristics. To the best of our knowledge, this is the largest case series with histopathological diagnosis published, filling an important gap in the literature.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.346
Teacher spread0.304 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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