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Record W2318963747 · doi:10.1017/s031716710000737x

Sporadic Creutzfeldt-Jakob Disease with Worsening Depression and Cognition

2007· article· en· W2318963747 on OpenAlexaffvenue
Mark E. Hudon, Richard Farb, Taim Muayqil, Zaeem A. Siddiqi

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPrion Diseases and Protein Misfolding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsApathyDepression (economics)Mental status examinationPsychologyMoodNeurological examinationPsychiatryMedicineAnxietyInternal medicinePediatricsCognition

Abstract

fetched live from OpenAlex

A 58-year-old male presented with a one-year history of low mood, early morning awakening from sleep, apathy, difficulty with memory, concentration and organization. This had been associated with intrusive concerns of a recent social stressor. He was no longer able to work and was on medical disability. Except for a 20kg weight loss there were no other constitutional or neurological symptoms. He had hypertension and hypercholesterolemia and was on atorvastatin and aspirin. He scored 28/30 on mini-mental status examination (MMSE) with errors on object recall; however he could recall forgotten items after cueing. He had difficulty with concentration, was apathic andhad a negative outlook to the future. His neurological examination and a detailed hematological work up including chemistry, cell counts, vitamin B12, folate, and renal, hepatic and thyroid function tests were normal. A brain magnetic resonance image (MRI) showed mild cerebral atrophy. Based on a formal neuropsychological assessment he was diagnosed with depression and started on Venlafaxine.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.255
Teacher spread0.239 · 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
Published2007
Admission routes2
Has abstractyes

Explore more

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