Can genetics inform the management of cognitive deficits in schizophrenia?
Bibliographic record
Abstract
There is no doubt that schizophrenia has a significant genetic component and a number of candidate genes have been identified for this debilitating disorder. Of note, several of these are implicated in cognition. Cognitive deficits constitute core symptoms of schizophrenia, and while current antipsychotic treatment strategies aim to help psychosis-related symptomatology, the cognitive symptom domain is largely inadequately treated. A number of other pharmacological approaches (e.g. using drugs that target specific neurotransmitter systems) have also been attempted for the amelioration of cognitive deficits in this population; however, these too have had limited success so far. Psychological interventions appear promising, though there has been speculation regarding whether or not these produce long-term functional improvements. Pharmacogenetic studies of the cognitive effects of currently available antipsychotics, although in relatively early stages, suggest that the treatment of cognitive deficits in schizophrenia may be advanced by focusing on genetic variants associated with specific cognitive dysfunctions in the general population and using this to match the most relevant pharmacological and/or psychological interventions with the genetic and cognitive profiles of the target population. Such a strategy would encourage bottom-up advances in drug development and provide a platform for individualised treatment of cognitive deficits in schizophrenia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".