Developing Predictive Animal Models and Establishing a Preclinical Trials Network for Assessing Treatment Effects on Cognition in Schizophrenia
Bibliographic record
Abstract
Animal models are an essential initial phase in the discovery of novel drugs to treat psychiatric disorders. At the sixth Measurement and Treatment Research to Improve Cognition in Schizophrenia conference, "New Approaches to Assessing and Improving Cognition in Schizophrenia," a discussion group was formed to address issues related to the development of predictive animal models of cognition that may be used as preclinical assays for putative cognitive enhancers. We identified 2 complementary approaches used to model cognitive impairments in animals. First, basic lesion/pharmacological models provide information about the particular neural substrates that may underlie different types of cognitive deficits found in schizophrenia. Findings from these studies can be mapped onto the second, more elaborate and etiologically relevant neurodevelopmental models of the disorder to ascertain which cognitive systems may be altered by early developmental insults. Particular attention must be given to the types of animal tasks used, in order to relate directly to the cognitive domains that are affected in schizophrenia patients. Importantly, the validation and standardization of the methodologies used in these preclinical assays would require the establishment of a preclinical trials network, serving as a counterpart to the recently established Treatment Units for Research on Neurocognition and Schizophrenia. The need to validate specific approaches to assess cognitive functions relevant to schizophrenia could be satisfied by a concerted effort enabled by a new funding directive from the National Institute of Mental Health with the explicit purpose of facilitating research on these models and assessing novel drug therapies that may be used to ameliorate the 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 | 0.120 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".