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Record W2164125047 · doi:10.1212/wnl.57.12.2278

Parkinsonism in Ontario

2001· article· en· W2164125047 on OpenAlexaffabout
Mark Guttman, Pamela M. Slaughter, M E Thériault, Donald P. DeBoer, C. David Naylor

Bibliographic record

VenueNeurology · 2001
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsParkinsonismMedicineNeurosciencePsychologyPathologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: PD was associated with increased mortality before levodopa therapy became available. There have been conflicting reports of PD mortality in the modern era. OBJECTIVE: To assess current mortality rates in a large unselected population receiving treatment for parkinsonism (PKM) followed for up to 6 years. METHODS: Cases were identified using linked administrative databases, including physician service and prescription drug claims, generated in Ontario's universal health insurance system. Control subjects were identified from the provincial registry of citizens and age and sex matched to cases. Comparative mortality was evaluated over the 6-year period of the study (1993/94 to 1998/99). The sensitivity of the findings was tested with differing case definitions. RESULTS: In 1993, 15,304 patients with PKM were identified and were age and sex matched to 30,608 control subjects (1:2 ratio). Over the study period, 50.8% (7,779) of the cases with PKM died compared with 29.1% (8,899) of the control subjects. The cases with PKM had an overall mortality odds ratio of 2.5 (95% CI: 2.4, 2.6) compared with the control group. Results were consistent whether cases were defined by physician diagnosis, use of anti-PD drugs, or both criteria. CONCLUSION: Despite modern drug therapy, PKM continues to confer a sharply increased mortality on unselected patients followed for several years.

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)0.001
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 score0.057
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.001

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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

Citations46
Published2001
Admission routes2
Has abstractyes

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