MétaCan
Menu
Back to cohort
Record W2171539930 · doi:10.1016/s1474-4422(07)70109-6

Effect of rivastigmine on delay to diagnosis of Alzheimer's disease from mild cognitive impairment: the InDDEx study

2007· article· en· W2171539930 on OpenAlexaff
Howard Feldman, Steven H. Ferris, Bengt Winblad, Nikolaos Sfikas, Linda Mancione, Yunsheng He, Sibel Tekin, Alistair Burns, Jeffrey L. Cummings, Teodoro del Ser, Domenico Inzitari, Jean-Marc Orgogozo, Heinrich Sauer, Philip Scheltens, Elio Scarpini, Nathan Herrmann, Martin R. Farlow, Steven G. Potkin, H. Cecil Charles, Nick C. Fox, Roger Lane

Bibliographic record

VenueThe Lancet Neurology · 2007
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsSunnybrook Health Science CentreHealth Sciences CentreUniversity of British Columbia Hospital
FundersNovartis PharmaNational Institutes of HealthNovartis
KeywordsRivastigmineMedicinePlaceboAdverse effectInternal medicineDementiaClinical trialClinical Dementia RatingClinical endpointCognitive declineDonepezilDisease

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.357
Teacher spread0.327 · 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

Citations337
Published2007
Admission routes1
Has abstractno

Explore more

Same venueThe Lancet NeurologySame topicDementia and Cognitive Impairment ResearchFrench-language works237,207