Speaker 2: Tao Li, China
Bibliographic record
Abstract
In contrast to the traditional viewpoint of psychopathology, which compulsivity [a repetitive ritualistic behavior which persists albeit its inappropriateness to the given situation and often result in undesirable consequences] and impulsivity [which encompasses actions that are insufficiently conceived, prematurely expressed, excessively risky or inappropriate to the situation, and that often lead to undesirable outcomes] were positioned at opposite ends of a behavioral characteristics, recent neuroimaging studies raise the possibility of compulsivity and impulsivity being orthogonal factors that each contribute in varying degrees to various psychiatric conditions, including obsessive-compulsive disorder (OCD).In this presentation, I firstly will discuss two differential facets of compulsivityimpulsivity using the clinical (compulsion sub-score of Y-BOCS) and neurocognitive (stop signal task) measures and examined their neural underpinning as reflected in the small-world network of structural (DTI) and resting state functional connectivity network in OCD subjects.Later on, this presentation will also discuss the relationship between OCD and substancerelated and addictive disorders (as defined in the DSM-5) from the spectrum-wise perspective of compulsivity and impulsivity, also using the frame of structural and functional connectome, to widen our understanding for clinical and neurocognitive dimensions of compulsivity-impulsivity phenomena.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.585 | 0.185 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".