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Record W2535870247 · doi:10.1212/wnl.0000000000003222

Clinical Reasoning: A complicated case of MELAS

2016· article· en· W2535870247 on OpenAlexaff
Nikkie Randhawa, Laura Wilson, Sharanpal Mann, Sandra Sirrs, Oscar Benavente

Bibliographic record

VenueNeurology · 2016
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineRadiologyPast medical historySurgeryCardiologyInternal medicine

Abstract

fetched live from OpenAlex

A 48-year-old right-hand-dominant woman presented to the emergency department with sudden onset of visual loss of her left visual field. She had a complicated medical history—end-stage renal disease requiring renal transplant 6 years prior to presentation secondary to biopsy-proven focal segmental glomerulosclerosis (FSGS), coronary artery disease, left atrial enlargement due to pulmonic stenosis, progressive sensorineural hearing loss 10 years prior and subsequent placement of a right cochlear implant, hypertension, and type 2 diabetes mellitus for the last year. At presentation, the patient's medications included cotrimoxazole prophylaxis, mycophenolate mofetil, tacrolimus, insulin, and folic acid. On examination, she was of short stature and had a low-grade fever and a left homonymous hemianopsia (L-HH). Within the next 3 weeks, she subsequently developed further visual disturbance to the point of being cortically blind in addition to new left upper extremity paresthesias. Initial CT head scan showed a subacute right occipital infarct and bilateral basal ganglia calcification. Subsequent CT imaging done 3 weeks later demonstrated interval development of new left temporoparietal and left occipital infarcts (figure, A). CT angiogram showed intracranial stenoses affecting basilar artery, both posterior cerebral arteries, and to a lesser degree bilateral anterior cerebral arteries and middle cerebral arteries, suggestive of possible atherosclerosis, but were not believed to be hemodynamically significant (figure, B). Due to her cochlear implant, significant streak artifact affected the interpretation of her CT scans and precluded her from obtaining an MRI.

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)0.006
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.335
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 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

Citations3
Published2016
Admission routes1
Has abstractyes

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