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Record W4401629460 · doi:10.1212/cpj.0000000000200345

A General Neurologist's Practical Diagnostic Algorithm for Atypical Parkinsonian Disorders

2024· review· en· W4401629460 on OpenAlex
Michiko Kimura Bruno, Rohit Dhall, Antoine Duquette, Ihtsham Haq, Lawrence S. Honig, Guillaume Lamotte, Zoltán Mari, Nikolaus R. McFarland, Leila Montaser-Kouhsari, Federico Rodríguez‐Porcel, Jessica Shurer, Junaid Siddiqui, C. Chauncey Spears, Anne‐Marie Wills, Kristophe Diaz, Lawrence I. Golbe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurology Clinical Practice · 2024
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Neurological Disorders and StrokeRobert Wood Johnson Medical School, Rutgers, The State University of New JerseyCleveland ClinicParkinsonfondenCurePSPNational Institutes of Health
KeywordsComputer scienceAlgorithmPsychiatryMedicinePsychology

Abstract

fetched live from OpenAlex

Purpose of Review: The most common four neurodegenerative atypical parkinsonian disorders (APDs) are progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal syndrome (CBS), and dementia with Lewy bodies (DLB). Their formal diagnostic criteria often require subspecialty experience to implement as designed and all require excluding competing diagnoses without clearly specifying how to do that. Validated diagnostic criteria are not available at all for many of the other common APDs, including normal pressure hydrocephalus (NPH), vascular parkinsonism (VP), or drug-induced parkinsonism (DIP). APDs also include conditions of structural, genetic, vascular, toxic/metabolic, infectious, and autoimmune origin. Their differential diagnosis can be challenging early in the course, if the presentation is atypical, or if a rare or non-neurodegenerative condition is present. This review equips community general neurologists to make an early provisional diagnosis before, or in place of, referral to a tertiary center. Early diagnosis would allay diagnostic uncertainty, allow prompt symptomatic management, provide disease-specific information and support resources, avoid further pointless testing and treatments, and create the possibility of trial referral. Recent Findings: We address 64 APDs using one over-arching flow diagram and a series of detailed tables. Most instances of APDs can be diagnosed with a careful history and neurological exam, along with a non-contrast brain MRI. Additional diagnostic tests are rarely needed but are delineated where applicable. Our diagnostic algorithm encourages referral to a tertiary center whenever the general neurologist feels it would be in the patient's best interest. Our algorithm emphasizes that the diagnosis of APDs is an iterative process, refined with the appearance of new diagnostic features, availability of new technology, and advances in scientific understanding of the disorders. Clinicians' proposals for all diagnostic tests for the APDs, including repeat visits, should be discussed with patients and their families to ensure that the potential information to be gained aligns with their larger clinical goals. Summary: We designed this differential diagnostic algorithm for the APDs to enhance general neurologists' diagnostic skills and confidence and to help them address the less common or more ambiguous cases.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.040
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.966
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0000.002

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 head0.112
GPT teacher head0.483
Teacher spread0.371 · 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