Toward New Avenues in the Treatment of Nonsuicidal Self-Injury
Bibliographic record
Abstract
The treatment of self-injury, or self-destruction of one's own body tissue, has become a new focus for both researchers and clinicians. Traditionally, the field of self-injury has distinguished between the behaviors exhibited among individuals with a developmental disability (self-injurious behaviors [SIBs]) and those present within a normative population (nonsuicidal self-injury [NSSI]). Despite this distinction, many pharmacotherapies for self-injury have been administered for both populations. The current review begins by summarizing the available efficacy studies investigating common pharmacological interventions in the treatment of self-injury. These studies are organized based on the most empirically supported neurochemical pathways in the development or maintenance of NSSI: endogenous opiods and monoamines. Although significant advances have been made in the field, conclusions based on efficacy studies of the pharmacological interventions used in the treatment of self-injury have been somewhat inconsistent. Finally, the review includes a discussion about potential avenues in the pharmacological treatment of NSSI via animal models of self-injury. Animal models present a unique opportunity to test neurobiological theories of self-injury using a controlled, systematic approach. Clinical considerations are presented as they relate to the available research findings and best practices in the treatment of self-injury.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".