Thiazolidinediones: novel treatments for cognitive deficits in mood disorders?
Bibliographic record
Abstract
The aim of this review is to provide a rationale for evaluating thiazolidinediones (TZDs) as putative treatments for cognitive deficits in individuals with mood disorders. A MedLine search of all English-language articles published between January 1966 and August 2006 was conducted. The search terms were: the non-proprietary names of TZDs (e.g., rosiglitazone and pioglitazone), peroxisome proliferator-activated receptor, cognition, neuroprotection, inflammation, oxidative stress, cellular metabolism and excitotoxicity cross-referenced with the individual names of mood (e.g., major depressive disorder and bipolar disorder) and dementing disorders (e.g., Alzheimer's disease) as defined in the Diagnostic and Statistical Manual of Mental Disorders third edition, revised/fourth edition, text revision (DSM-III-R/IV-TR). The search was augmented with a manual review of article reference lists. Articles selected for review were based on adequacy of sample size, the use of standardized experimental procedures, validated assessment measures and overall manuscript quality. Contemporary pathophysiologic models of mood disorders emphasize alterations in neuronal plasticity, metabolism and cytoarchitecture with associated regional abnormalities in neuronal (and glial) density and morphology. These abnormalities are hypothesized to subserve cognitive deficits and other clinical features of mood disorders. TZDs may attenuate, abrogate and/or reverse the neurotoxic effects of depressive illness by means of disparate mechanisms, notably insulin signaling, anti-inflammation, glucocorticoid activity and cellular metabolism. Extant data provide the basis for formulating a hypothesis that TZDs may be salutary for cognitive deficits and several aspects of somatic health (e.g., cardiovascular disease) associated with mood 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".