MétaCan
Menu
Back to cohort
Record W1966938646 · doi:10.1002/mds.22997

Impact of belief in neuroprotection on therapeutic intervention in Parkinson's disease

2010· article· en· W1966938646 on OpenAlexaff
Rodger J. Elble, Oksana Suchowersky, Stephanie R. Shaftman, William J. Weiner, Peng Huang, Barbara C. Tilley

Bibliographic record

VenueMovement Disorders · 2010
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of Calgary
FundersNational Institute of Neurological Disorders and Stroke
KeywordsNeuroprotectionParkinson's diseaseRating scaleIntervention (counseling)PlaceboDiseaseMedicineDegenerative diseaseClinical trialPsychologyPhysical therapyPsychiatryInternal medicineAlternative medicinePathologyDevelopmental psychology

Abstract

fetched live from OpenAlex

We explored the hypotheses that an investigator's belief in a putative neuroprotective agent might influence the timing of symptomatic intervention and the assessment of signs and symptoms of patients with Parkinson's disease with the Unified Parkinson's Disease Rating Scale (UPDRS). These hypotheses were tested with Cox and general linear modeling, using data from a previously published double-blind placebo-controlled futility trial of coenzyme Q(10) and GPI-1485. We found the investigators' level of confidence in these agents had no effect on the time to symptomatic therapy or on the change in UPDRS during 12 months of treatment.

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)0.040
metaresearch head score (Gemma)0.189
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 score0.040
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.189
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.306
Teacher spread0.294 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueMovement DisordersSame topicComputational Drug Discovery MethodsFrench-language works237,207