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Record W2583906556 · doi:10.1002/9781118634523.ch2

Clinical and Economic Features of Age‐Related Neurological Diseases

2017· other· en· W2583906556 on OpenAlexaff
Christopher A. Shaw

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAmyotrophic lateral sclerosisFrontotemporal dementiaNeuroscienceDementiaPhysical medicine and rehabilitationMovement disordersDiseaseAtaxiaPsychologyWeaknessMedicinePathologyAnatomy

Abstract

fetched live from OpenAlex

This chapter considers Parkinson's, amyotrophic lateral sclerosis (ALS), and Alzheimer's - the archetypical neurological diseases associated with middle and old age. Parkinson's disease's symptoms include stiffness of the extremities, clumsiness, slowness of movement, decreased arm swings when walking, decreased facial expression, and various dyskinesias, or movement disorders. As Parkinson's disease progresses, a characteristically stooped posture emerges, along with akinesia, ataxia, bradykinesia, and festination. In ALS, a type of motor neuron disease (MND), motor weakness and deficits are commonly first observed in the legs or arms, a sign indicating the loss of lower motor neurons. Pathologically, Alzheimer's disease features damage to the parts of the brain primarily involved in memory and learning, notably regions of the neocortex and hippocampus. Alzheimer's-like dementias can combine features of other neurological diseases, including some of the parkinsonisms (e.g., ALS-PDC of the various locations in the Western Pacific, PSP, G-PDC) and disorders such as frontotemporal dementia (FTD).

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.003

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 head0.025
GPT teacher head0.325
Teacher spread0.299 · 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
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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