Biomarkers in geriatric psychiatry: searching for the holy grail?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Biomarkers in clinical medicine are used to detect or diagnose specific illnesses, predict disease progression, and predict the response to treatment. As the proportion of adults over 65 years of age rises, there is a growing need to detect neurodegenerative disease at an earlier stage with the goal of improving treatment for highly prevalent illnesses such as late-life depression and dementia. RECENT FINDINGS: The search for biomarkers of late-life mental disorders includes the exploration of structural neuroimaging, functional neuroimaging, genomics, proteomics, noninvasive neurophysiology, cerebrospinal fluid, and plasma analysis. Novel structural and functional neuroimaging techniques that have recently been developed show promise as biomarkers of both late-life depression and specific dementia syndromes. The fields of proteomics and genomics are advancing our ability to identify genes and aberrant proteins that detect preclinical dementia. As depression is often a harbinger of dementia in late life, recent studies are beginning to elucidate the relationship between different types of late-life depression and the subsequent emergence of dementia. SUMMARY: Biomarker research in late-life mental disorders is progressing at a rapid pace. The application of current biomarkers to clinical practice may be on the horizon with further research that refines their sensitivity and specificity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".