Validation and assessment of the self-injurious behavior scale for tic disorders (SIBS-T)
Bibliographic record
Abstract
Self-injurious behavior (SIB) is a well-known phenomenon in patients with chronic tic disorders (CTD). To investigate prospectively symptomatology of SIB in adults with CTD, we developed and validated the self-injurious behavior scale for tic disorders (SIBS-T). Patients completed the SIBS-T and a variety of assessments for tics and comorbidities. We investigated SIB frequency, internal consistency of the SIBS-T, and carried out an exploratory factor analysis (EFA). We enrolled n = 123 adult patients with CTD. SIB was reported by n = 103 patients (83.7%). The most frequently reported SIB were beating/pushing/throwing and were found in 79.6% of cases. Patients with SIB had significantly higher tic severity measured with the Adult Tic Questionnaire (ATQ) (p = 0.002) as well as higher severity of psychiatric comorbidities such as obsessive-compulsive symptoms (OCS) (p < 0.001,), attention deficit/hyperactivity disorder (ADHD) (p < 0.001,), and anxiety (p = 0.001). In addition, patients with SIB had significantly lower quality of life (p = 0.002). Pearson correlations demonstrated significant associations between SIB and severity of tics (p < 0.001), depression (p = 0.005), ADHD (p = 0.008), and borderline personality traits (p = 0.014). Consequently, higher SIBS-T also correlated with greater impairment of quality of life (p < 0.001). The internal consistency of the SIBS-T was good (α = 0.88). The EFA confirmed a single factor underlying the SIBS-T.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".