Trichotillomania in youth: a retrospective case series
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to investigate the outcome of the naturalistic treatment of youth with Trichotillomania (TTM) in an anxiety disorders clinic sample. METHODS: A retrospective chart review was conducted on 11 treated patients between the ages of 6 and 17, with DSM-IV TTM. RESULTS: Ten patients were initially treated with a serotonin reuptake inhibitor (SRI), whereas one patient was initially treated with an antipsychotic. Three of the 10 patients who started with an SRI had a response (Clinical Global Impression-Improvement Scale (CGI-I)>or=2) in TTM symptoms. Nine patients of the 11 patients were treated with an antipsychotic medication (in 8 patients the antipsychotic was added after an initial trial with an SRI, in 1 patient the antipsychotic was the first line agent), 2 patients remained on an SRI; 8/9 were responders to antipsychotic treatment and 2 patients remitted (complete cessation of hair pulling). Adverse events to the SRI or antipsychotic were experienced by 7/11 patients but did not lead to treatment discontinuation. CONCLUSIONS: This retrospective case series suggests that youth with TTM maybe responsive to pharmacological interventions with SRIs and/or antipsychotic agents, although the response seemed to be more robust with antipsychotics. These preliminary findings will need to be replicated in a larger scale controlled design.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".