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Record W4406024146 · doi:10.1002/alz.092400

1‐ versus 2‐test criteria for cognitive impairment and associations with CSF and imaging markers in former American football players

2024· article· en· W4406024146 on OpenAlexaff
Monica T. Ly, Caroline Altaras, Yorghos Tripodis, Charles H. Adler, Laura J. Balcer, Charles Bernick, Henrik Zetterberg, Kaj Blennow, Elaine R. Peskind, Sarah J. Banks, William Barr, Jennifer V. Wethe, Mark W. Bondi, Lisa Delano‐Wood, Robert C. Cantu, Michael Coleman, David W. Dodick, Jesse Mez, Joseph Palmisano, Brett Martin, Alexander Lin, Sylvain Bouix, Jeffrey L. Cummings, Eric M. Reiman, Martha E. Shenton, Robert A. Stern, Michael L. Alosco

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPsychologyStroop effectEpisodic memoryAudiologyCognitive testExecutive dysfunctionNeuropsychologyMemory impairmentMedicineCognitionClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract Background Clinically meaningful cognitive impairment has typically been defined as a single impaired test score, but this approach is prone to false‐positive errors. Examining two test scores at a lower threshold (i.e., using neuropsychological criteria) can improve diagnostic reliability and has shown stronger associations with biomarkers of Alzheimer’s disease. Cognitive impairment in episodic memory and/or executive functioning is a core feature of traumatic encephalopathy syndrome (TES). However, there remains a need to improve the specificity of TES criteria. We applied 1‐ vs. 2‐test criteria for cognitive impairment in former American football players to examine whether 2‐test criteria showed stronger associations with biomarkers of tau, axonal injury, and neurodegeneration. Method 169 male former American football players from the DIAGNOSE CTE Research Project completed neuropsychological assessment, lumbar puncture, and MRI (see Table). Episodic memory measures were delayed recall from Craft Story, Brief Visuospatial Memory Test, and NAB List Learning. Executive functioning measures were FAS, Stroop Interference, NAB Mazes, and Trails B. Cerebrospinal fluid (CSF) was measured using Lumipulse technology (p‐tau, t‐tau) and an in‐house ELISA (neurofilament light [NfL]). Hippocampal volumes were extracted from structural MRI using Freesurfer 7.1. Cognitive impairment was identified by 1‐test criteria (≥1.5 SD below norms on one test in either domain) and 2‐test criteria (>1 SD below norms on two tests within a domain). Regressions adjusting for age, race, education, and APOE ε4 status assessed whether meeting 1‐ or 2‐test criteria, separately, predicted log‐transformed CSF p‐tau181, p‐tau231, t‐tau, and NfL, and hippocampal volumes. Result 37 of the 99 football players that were impaired by 1‐test criteria did not meet 2‐test criteria, and four uniquely met 2‐test but not 1‐test criteria (see Figure). Cognitive impairment by 2‐test but not 1‐test criteria predicted higher log‐CSF NfL (2‐test: B = 0.24, p = .007; 1‐test: B = 0.06, p = .53), smaller left hippocampal volume (2‐test: B = ‐195.84, p = .01; 1‐test: B = ‐131.52, p = .08), and smaller right hippocampal volume (2‐test: B = ‐161.19, p = .045; 1‐test: B = ‐134.88, p = .08). Neither criterion predicted p‐tau181, p‐tau231, or t‐tau. Conclusion 2‐test criteria demonstrated stronger associations between cognitive impairment and markers of axonal injury and neurodegeneration. Incorporating multiple test scores to identify cognitive impairment could improve diagnostic specificity in TES.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.051
GPT teacher head0.365
Teacher spread0.314 · 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
Published2024
Admission routes1
Has abstractyes

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