MétaCan
Menu
← Back to cohort
Record W2534896374 · doi:10.1016/j.jalz.2016.06.1357

P2‐190: Diagnostic Disagreement among Major Consensus Criteria for Alzheimer's Disease when Compared to the Nincds‐Adrd

2016· article· en· W2534896374 on OpenAlexaff
Benjamin Lam, Alexandra Kim, Kie Honjo, Isabel Lam, Alex Kiss, Morris Freedman, Donald T. Stuss, Sandra E. Black, Mario Masellis

Bibliographic record

VenueAlzheimer s & Dementia · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsBaycrest HospitalToronto Dementia Research AllianceUniversity of TorontoSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsDementiaDiseasePsychologyMedicineGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Major consensus criteria currently in use include: (1) National Institute on Aging-Alzhiemer’s Association (NIA-AA), (2) International Working Group (IWG), (3) International Classification of Diseases (ICD-10), and (4) Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The National Institute of Neurological and Communicative Disorders – Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criterion remains an important comparator standard given its role in AD research prior to the advent of the newer, biomarker-driven criteria. A preliminary study of participants from the Sunnybrook Dementia Study (SDS; ClinicalTrials.gov NCT01800214) demonstrated notable discordance between criteria, in particular between the subtype and co-pathology permissive NIA-AA, and the prototypic and biomarker-requiring IWG. This study examines diagnostic agreement between these new criteria and the NINCDS-ADRDA “bronze standard”. A convenience sample of 155 participants from the SDS who met NINCDS-ADRDA criteria for probable or possible AD was reviewed retrospectively. Data included clinical history, function (Alzheimer’s Disease Functional Assessment of Change Scale), cognitive screening (MMSE and Behavioural Neurology Assessment), cognitive testing (Dementia Rating Scale), MRI, and single photo emission computed tomography (SPECT). SPECT was used in place of FDG-PET when applying the NIA-AA and IWG criteria. Diagnostic re-classification by new criteria is show in Table 1 and Table 2. Comparing across broad diagnostic categories (AD vs. not AD), agreement with the NINCDS-ADRDA was best for the NIA-AA (94%) and DSM-5 (96%), and poor for the IWG-1 (54%) and ICD-10 (55%). Agreement with the NINCDS-ADRDA probable AD subgroup was better for the IWG (76%) and ICD-10 (71%), and much worse for the possible AD subgroup – IWG (13%) and ICD-10 (27%). Individuals diagnosed with AD by the NINCDS-ADRDA will generally still be diagnosed with AD by the NIA-AA and DMS-5. However, a significant portion will not, using the IWG or ICD-10, especially among those previously diagnosed with only possible disease.

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)0.017
metaresearch head score (Gemma)0.041
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 score0.017
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.265
GPT teacher head0.401
Teacher spread0.135 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicHealth Systems, Economic Evaluations, Quality of Life→French-language works237,207→