White matter hyperintensities – from medical comorbidities to bipolar disorders and back
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".