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Record W1978093816 · doi:10.4088/jcp.10m06382oli

The Diagnostic Challenge of Psychiatric Symptoms in Neurodegenerative Disease

2011· article· en· W1978093816 on OpenAlexfundno aff
Josh Woolley, Baber K. Khan, Nikhil K. Murthy, Bruce L. Miller, Katherine P. Rankin

Bibliographic record

VenueThe Journal of Clinical Psychiatry · 2011
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Cancer InstituteNational Institute of Mental HealthNational Institute on AgingCanadian Centre for Applied Research in Cancer Control
KeywordsFrontotemporal dementiaProgressive supranuclear palsyPsychiatryDementiaSemantic dementiaCorticobasal degenerationFamily historyDiseaseMedicinePrimary progressive aphasiaSchizophrenia (object-oriented programming)Medical diagnosisDepression (economics)Internal medicinePathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify rates of and risk factors for psychiatric diagnosis preceding the diagnosis of neurodegenerative disease. METHOD: Systematic, retrospective, blinded chart review was performed of 252 patients with a neurodegenerative disease diagnosis seen in our specialty clinic between 1999 and 2008. Neurodegenerative disease diagnoses included behavioral-variant frontotemporal dementia (n = 69), semantic dementia (n = 41), and progressive nonfluent aphasia (n = 17) (all meeting Neary research criteria); Alzheimer's disease (n = 65) (National Institute of Neurologic and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association research criteria); corticobasal degeneration (n = 25) (Boxer research criteria); progressive supranuclear palsy (n = 15) (Litvan research criteria); and amyotrophic lateral sclerosis (n = 20) (El Escorial research criteria). Reviewers remained blinded to each patient's final neurodegenerative disease diagnosis while reviewing charts. Extensive caregiver interviews were conducted to ensure accurate and reliable diagnostic histories. For each patient, we recorded history of psychiatric diagnosis, family psychiatric and neurologic history, age at symptom onset, and demographic information. RESULTS: A total of 28.2% of patients with a neurodegenerative disease received a prior psychiatric diagnosis. Depression was the most common psychiatric diagnosis in all groups. Behavioral-variant frontotemporal dementia patients received a prior psychiatric diagnosis significantly more often (50.7%; P < .001) than patients with Alzheimer's disease (23.1%), semantic dementia (24.4%), or progressive nonfluent aphasia (11.8%) and were more likely to receive diagnoses of bipolar disorder or schizophrenia than were patients with other neurodegenerative diseases (P < .001). Younger age (P < .001), higher education (P < .05), and a family history of psychiatric illness (P < .05) increased the rate of prior psychiatric diagnosis in patients with behavioral-variant frontotemporal dementia. Cognitive, behavioral, and emotional characteristics did not distinguish patients who did or did not receive a prior psychiatric diagnosis. CONCLUSIONS: Neurodegenerative disease is often misclassified as psychiatric disease, with behavioral-variant frontotemporal dementia patients at highest risk. While this study cannot rule out the possibility that psychiatric disease is an independent risk factor for neurodegenerative disease, when patients with neurodegenerative disease are initially classified with psychiatric disease, the patient may receive delayed, inappropriate treatment and be subject to increased distress. Physicians should consider referring mid- to late-life patients with new-onset neuropsychiatric symptoms for neurodegenerative disease evaluation.

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)0.005
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.049
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.062
GPT teacher head0.398
Teacher spread0.335 · 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 designNot applicable
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

Citations546
Published2011
Admission routes1
Has abstractyes

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