Biomarkers for military mental health: Insights, challenges, and future prospects
Bibliographic record
Abstract
Mental health is increasingly being recognized as both a leading cause of disability worldwide and an important area of opportunity for biological breakthroughs. A major limitation in the current diagnosis and management of severe psychiatric conditions is the exclusive reliance on subjective clinical information in the absence of available laboratory tests. A lack of objective biomarkers that reliably identify mental health disorders, and which could serve as targets for diagnosis, treatment response monitoring, and the development of novel therapeutics, remains a fundamental challenge of psychiatry today. Although clinical tests are well established in other areas of medicine, their development in psychiatry has been relatively slow. So far, no biomarkers or other risk markers are available to create profiles to enhance prediction and therapeutic selection in psychiatry. As novel ‘omics-based technologies – such as genomics, proteomics, and metabolomics – and advanced imaging modalities enable researchers to probe the molecular to systemic underpinnings of various disorders, opportunities arise to explore the biological basis for mental health and disease. It is anticipated that specific alterations in blood-based molecular biomarkers, such as DNA, RNA, protein, and metabolite levels, will lead to standardized tests to facilitate diagnosis as they reflect the underlying etiology and mechanisms of disease. They may also pave the way for earlier and more effective treatment and monitoring of patients. Ultimately, the coordinated effort of relevant civilian and military stakeholders – including researchers, physicians and funders – together with standardization initiatives, will be vital to overcoming existing challenges to advance personalized mental health care using sensitive and specific biomarkers.
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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".