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Record W4317888316 · doi:10.46747/cfp.690120

Parkinson disease primer, part 1: diagnosis

2023· article· en· W4317888316 on OpenAlexaffvenueabout
Chris Frank, Ruth Chiu, Joyce Lee

Bibliographic record

VenueCanadian Family Physician · 2023
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of Alberta HospitalToronto General HospitalQueen's University
Fundersnot available
KeywordsParkinsonismMedicineDiseaseParkinson's diseaseLevodopaMotor symptomsReferralPediatricsPathologyFamily medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide family physicians an updated approach to the diagnosis of Parkinson disease (PD). SOURCES OF INFORMATION: Published guidelines on the diagnosis and management of PD were reviewed. Database searches were conducted to retrieve relevant research articles published between 2011 and 2021. Evidence levels ranged from I to III. MAIN MESSAGE: Diagnosis of PD is predominantly clinical. Family physicians should evaluate patients for specific features of parkinsonism, then determine whether symptoms are attributable to PD. Levodopa trials can be used to help confirm the diagnosis and alleviate motor symptoms of PD. "Red flag" features and absence of response to levodopa may point to other causes of parkinsonism and prompt more urgent referral. CONCLUSION: Access to neurologists and specialized clinics varies, and Canadian family physicians can be important players in facilitating early and accurate diagnosis of PD. Applying an organized approach to diagnosis and considering motor and nonmotor symptoms can greatly benefit patients with PD. Part 2 in this series will review management of PD.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.029
GPT teacher head0.255
Teacher spread0.226 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2023
Admission routes3
Has abstractyes

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