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Record W2794682176 · doi:10.1136/jnnp-2017-317839

Association between semantic dementia and progressive supranuclear palsy

2018· letter· en· W2794682176 on OpenAlexfundno aff
Julie S. Snowden, Christopher Kobylecki, Matthew Jones, J. C. Thompson, Anna Richardson, David Mann

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2018
Typeletter
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersAlzheimer SocietyAlzheimer’s SocietyAlzheimer's Research TrustNational Cancer InstituteAlzheimer's Society
KeywordsProgressive supranuclear palsySemantic dementiaDementiaAssociation (psychology)MedicinePsychologyFrontotemporal dementiaPhysical medicine and rehabilitationPathologyDiseasePsychotherapist

Abstract

fetched live from OpenAlex

Clinical syndromes associated with frontotemporal lobar degeneration (FTLD) pathology may overlap. Progressive supranuclear palsy syndrome (PSPs) may co-occur with behavioural frontotemporal dementia (bvFTD), non-fluent aphasia (nfPPA) and corticobasal syndrome.1 This is unsurprising, given each syndrome’s association with tau pathology. We describe here a less anticipated association: between PSPs and semantic dementia (SD). A 72-year-old man presented with an 8-year history of difficulty understanding words and phrases and recognising people and places. No behavioural changes were reported. There was no relevant family history. Neurological examination was normal. Neuropsychological examination revealed a severe disorder of semantic, and to a lesser extent, episodic memory. He could not identify high-profile famous faces and names, reporting most to be unfamiliar. He named only 2/30 pictures on the Graded naming test and scored 46/52 and 38/52 on word and picture versions of the Pyramids and Palm trees test. He performed normally on perceptual and spatial tasks (Visual Object and Space Perception Battery), except where recognition of object identity was required. Sentence comprehension (Test of Reception of Grammar) and executive performance (Weigls blocks, Brixton) were preserved. Memory test scores were reduced. However, he was fully oriented in time and place raising the possibility that semantic impairment contributed to his poor scores. An initial MR brain scan showed marked anterior temporal lobe atrophy with right-sided predominance (figure 1A) and atrophy of the superior cerebellar peduncles (figure 1B). The clinical picture suggested SD, although with greater episodic memory loss than commonly found. Figure 1 Coronal T1-weighted MR brain imaging showing marked (A) anterior temporal lobe atrophy and (B) atrophic superior cerebellar peduncles (arrowhead). (C) Sagittal T1-weighted image showing midbrain atrophy (midbrain:pons ratio 0.47). Macroscopic image of brain showing temporal lobe atrophy, most marked …

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.260
Teacher spread0.246 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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