MétaCan
Menu
← Back to cohort
Record W2896640884 · doi:10.1016/j.jalz.2018.06.383

P1‐375: UTILITY OF MRI IN THE ASSESSMENT OF AD/MCI: FINDINGS FROM THE ONTARIO NEURODEGENERATIVE DISEASE RESEARCH INITIATIVE (ONDRI) STUDY

2018· article· en· W2896640884 on OpenAlexaffabout
Arunima Kapoor, Sean Symons, Robert Bartha, Christopher J.M. Scott, Sandra E. Black, Fuqiang Gao, Miracle Ozzoude, Michael Borrie, Paula McLaughlin, Donna Kwan, Jennifer Mandzia, Angela K. Troyer, Alisia Bonnick, Morris Freedman, Corinne E. Fischer, Andrew Frank, Dallas Seitz, Michael Wolf, Nicolaas Paul L.G. Verhoeff, Gary Naglie, William E. Reichman, Mario Masellis, Sara Mitchell, David F. Tang‐Wai, Maria Carmela Tartaglia, Sanjeev Kumar, Bruce G. Pollock, Tarek K. Rajji, Elizabeth Finger, Stephen Pasternak, Richard H. Swartz

Bibliographic record

VenueAlzheimer s & Dementia · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsCentre for Addiction and Mental HealthParkwood InstituteÉlisabeth Bruyère HospitalBaycrest HospitalCancer Care OntarioWestern UniversityUniversity of TorontoRobarts Clinical TrialsOccupational Cancer Research CentreHealth Sciences CentreSt. Michael's HospitalSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMagnetic resonance imagingAtrophyNeuroimagingDiseaseNeuropsychologyCognitive impairmentCognitionAlzheimer's Disease Neuroimaging InitiativeCohortPathologyRadiologyPsychiatry

Abstract

fetched live from OpenAlex

Current diagnostic criteria for Alzheimer's disease (AD) or Mild Cognitive Impairment (MCI) specifically do not recommend magnetic resonance imaging (MRI) for the diagnostic workup since universally accepted quantitative biomarkers have not been established. MRI can serve an important role in the diagnosis of AD or MCI by identifying comorbid pathologies that may alter treatment or prognosis. We aimed to determine the frequency with which inclusion of clinical review of MRI results detects comorbid vascular and non-vascular pathologies that could impact clinical management or alter findings from clinical trials and whether those with and without incidental findings have different degrees of cognitive impairment or brain atrophy. Hypotheses: There will be >5% of people with potentially important comorbid findings detected on MRI and those with and without comorbid findings (vascular or non-vascular) will differ on measures of cognition, function or medial temporal / hippocampal atrophy. Study sample will include all Ontario Neurodegenerative Disease Research Initiative (ONDRI) screened/consented patients who met diagnostic criteria for AD and MCI and completed an MRI scan. Patients with incidental findings will be identified. The proportion of AD/MCI patients with incidental MRI findings will be established and differences in demographic, neuropsychological, functional and medial temporal/hippocampal brain volume measures between those with and without incidental findings will be examined. One hundred and forty-one patients met clinical criteria for the ONDRI AD/MCI cohort. Preliminary data analysis revealed at least 22 cases (16%) of incidental MRI findings, of which 15 cases (11%) were identified as exclusionary pathology from the AD/MCI cohort. Imaging exclusions included large vessel strokes, smaller strategic infarcts, tumors and prior undisclosed neurosurgeries. Further analysis will elucidate whether those with and without incidental findings have different degrees of cognitive impairment or brain atrophy. The current absence of an MRI requirement in the diagnostic criteria of AD/MCI limits detection of alternate or comorbid conditions that could influence management of AD/MCI as well as comorbid conditions. Diagnostic guidelines should consider endorsing qualitative MRI results in disease diagnosis and management.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.302
Threshold uncertainty score0.600

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.133
GPT teacher head0.418
Teacher spread0.285 · 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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicDementia and Cognitive Impairment Research→French-language works237,207→