Bibliographic record
Abstract
Mood disorders - depression in particular - affect a large percentage of the population and account for a large part of worldwide burden both on a health and economic basis. Animal models are essential for expanding scientific knowledge of these disorders as they allow for specific and precise manipulations of the brain that are not possible in humans. However, because of the complexity and individual variability of depression, developing and assessing appropriate animal models is a major challenge. To further understand the causes of this variability, there has been an increased interest in the neurological underpinnings of the illness. This review will discuss the techniques used to assess and measure depression-like phenotypes in animals as well as models of the illness and tasks used to measure behavioural phenotypes. There has been increasing precision and sophistication in the development of animal models, from lesion to transgenic models, and advances in tasks from basic aversive tasks to more advanced touchscreen tasks. This review explores the use of animal models for depression and argues that touchscreen tasks may be better suited for assessing and measuring depression-like behaviour in rodent models as these tasks are less aversive, more translatable, and potentially more powerful in detecting subtle differences across treatment groups.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".