Managing psychiatric disorders with antidiabetic agents: translational research and treatment opportunities
Bibliographic record
Abstract
The objective of this paper is to synthesise extant studies describing the neurotherapeutic effects of antidiabetic agents in neuropsychiatric disorders. The authors conducted a MedLine search of all English-language articles published between 1966 and March 2006. The search terms were the nonproprietary names of established and putative antidiabetic agents (e.g., insulin, insulin secretagogues and sensitisers) cross-referenced with the individual names of Diagnostic and Statistical Manual of Mental Disorders (DSM)-III-R/IV/-TR-defined mood, psychotic, anxiety and dementing disorders. The search was augmented with a manual review of article reference lists. Contemporary models of disease pathophysiology in major depressive disorder, bipolar disorder and several dementing disorders (e.g., Alzheimer's disease) emphasise alterations in cellular plasticity and cytoarchitecture, with associated regional abnormalities in neuronal and glial density and morphology. Antidiabetic treatments (e.g., thiazolidinediones) may be capable of attenuating this pathological process via disparate mechanisms (e.g., neuroprotective, neurotrophic, anti-inflammatory). Enhanced insulin signalling with antidiabetic treatments may preserve and/or augment cognitive function in several neuropsychiatric disorders. Antidiabetic treatments, which maintain euglycaemia, hold promise as potent and clinically significant therapeutic interventions for several neuropsychiatric disorders.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".