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Record W3016292035 · doi:10.1212/wnl.0000000000009410

Clinical Reasoning: An 81-year-old woman with decreased consciousness and fluctuating right facial droop

2020· article· en· W3016292035 on OpenAlexaff
Randy Van Ommeren, Aaron Izenberg, Steven Shadowitz, Richard I. Aviv, Julia Keith

Bibliographic record

VenueNeurology · 2020
Typearticle
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineAbnormalityBlood pressureAnesthesiaHeart rateSurgeryCardiologyInternal medicine

Abstract

fetched live from OpenAlex

An 81-year-old woman presented to the hospital after a week of intermittent episodes of decreased level of consciousness, right facial droop, and slurred speech, lasting approximately 30 minutes. Her medical history included chronic obstructive pulmonary disease, dyslipidemia, orthostatic hypotension, and 2 previous TIAs. She had fallen once in the preceding week, presumably as a result of her drowsiness. On examination, she was afebrile with a heart rate of 81 bpm and a blood pressure of 108/65 mm Hg. She was alert and cooperative, but disoriented to the year and location. Language examination was normal. Cranial nerves, motor, coordination, and sensory examinations were all normal without any focal deficits. However, when assessed during one of her episodes, she was less responsive, had decreased verbal output, and was noted to have a right facial droop. Basic serologic tests (complete blood count, electrolytes, renal function tests) were unremarkable. A head CT showed bilateral subarachnoid hemorrhage over the cerebral hemispheres without further abnormality.

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)0.002
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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.027
GPT teacher head0.316
Teacher spread0.289 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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