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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".