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Record W2003611010 · doi:10.3109/07853890.2011.595733

White matter hyperintensities – from medical comorbidities to bipolar disorders and back

2011· review· en· W2003611010 on OpenAlexafffund
Eva Gunde, Ryan Blagdon, Tomáš Hájek

Bibliographic record

VenueAnnals of Medicine · 2011
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsHyperintensityEndophenotypeEtiologyBipolar disorderNeurologyMetabolic syndromeMedicineMigraineInternal medicinePsychologyPsychiatryMagnetic resonance imagingCognitionObesity

Abstract

fetched live from OpenAlex

White matter hyperintensities (WMHs) are among the most replicated neuroimaging findings in studies of patients with bipolar disorders (BD). Despite the high rates of WMHs, their role and etiology in BD are not well understood. WMHs occur in multiple other conditions frequently co-morbid with BD. From the available studies it seems that WMHs are not a primary risk factor/endophenotype for BD. More likely, these lesions indicate the presence of medical co-morbidities with specific links to BD. Furthermore, the etiology of the WMHs in BD may represent different processes depending on age. In certain forms of BD, such as pediatric BD, WMHs may represent co-morbidity with developmental disorders. High frequency of migraine in BD and high prevalence of WMHs in migraine may suggest that a substantial proportion of WMHs in early adulthood to midlife BD subjects may be related to co-morbidity with migraine. Among elderly subjects with BD, or those with late-onset BD, WMHs are likely related to the presence of cardiovascular/metabolic disorders. With further research WMHs may enhance our knowledge about various pathological pathways involved in BD, help in decreasing the etiological heterogeneity of BD, and become useful as markers of severity or subtype of BD.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.001

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 head0.109
GPT teacher head0.362
Teacher spread0.253 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations26
Published2011
Admission routes2
Has abstractyes

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