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Record W6963967476 · doi:10.25384/sage.c.6776821

Behavioral Impairments and Increased Risk of Cortical Atrophy Risk Scores Among World Trade Center Responders

2023· other· en· W6963967476 on OpenAlexaff

Bibliographic record

VenueSage Journals Data · 2023
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsAtrophyDementiaRisk factorWorld trade centerCognitionDiseaseDegenerative diseaseSensory system

Abstract

fetched live from OpenAlex

<b>Objective:</b> World Trade Center (WTC) responders are susceptible to both cognitive and neuropsychiatric impairments, particularly chronic posttraumatic stress disorder. The present study examined self-reported behavioral impairments in a sample of 732 WTC responders, 199 of whom were determined to have high risk of WTC-related cortical atrophy by an artificial neural network. <b>Results:</b> We found that responders at increased risk of cortical atrophy showed behavioral impairment across five domains: motivation, mood, disinhibition, empathy, and psychosis (14.6% vs 3.9% in the low-risk group; <i>P</i> = 3.90 × 10<sup>−7</sup>). Factor analysis models revealed that responders at high risk of cortical atrophy tended to have deficits generalized across all aspects of behavioral impairment with focal dysfunction in sensory psychosis. We additionally describe how relationships are modulated by exposure severity and pharmacological treatments. <b>Discussion:</b> Our findings suggest a potential link between sensory deficits and the development of cortical atrophy in WTC responders and may indicate symptoms consistent with a clinical portrait of parietal dominant Alzheimer's disease or a related dementia (ADRD). Results underscore the importance of investigating neuropsychiatric symptomatology in clinical evaluations of possible ADRD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.042
GPT teacher head0.299
Teacher spread0.258 · 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 teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

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