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Record W2507843403 · doi:10.14740/jmc.v7i9.2591

A Case of Transient Loss of Vision Following Coronary Angiography: Etiology, Investigation and Management

2016· article· en· W2507843403 on OpenAlexvenueno aff
Elizabeth McElnea, David Gallagher, Khaldoon Al-Tahs, Thomas J. Kiernan

Bibliographic record

VenueJournal of Medical Cases · 2016
Typearticle
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCortical blindnessCoronary angiographyBlindnessAngiographyEtiologyRadiologyContrast (vision)Contrast mediumMyocardial infarctionCerebral angiographyVisual DisturbanceVisual HallucinationComplicationCardiologyInternal medicineSurgeryOptometryAudiology

Abstract

fetched live from OpenAlex

Cortical blindness is, thankfully, a rarely encountered complication of coronary angiography. We present the case of a 72-year-old Caucasian gentleman in whom bilateral visual loss occurred abruptly after exposure to contrast during diagnostic coronary angiography. Areas of acute cerebral infarction were not appreciated at initial cranial computed tomography. Leakage of contrast medium into the occipital cortices was similarly absent. The patient recovered vision within 24 hours. Given the frequency with which coronary angiography is performed worldwide, an awareness of the causes of cortical blindness following the same is important. Although already well elaborated in the literature related to both cardiology and radiology, there are few reports in the general medical or ophthalmology literature that describe transient cortical blindness after coronary angiography and detail contrast-associated visual loss. J Med Cases. 2016;7(9):379-383 doi: http://dx.doi.org/10.14740/jmc2591w

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.309
Teacher spread0.287 · 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 designCase report
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
Published2016
Admission routes1
Has abstractyes

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