Self-harm and suicidal acts: a suitable case for treatment of impulsivity-driven behaviour with repetitive transcranial magnetic stimulation (rTMS)
Bibliographic record
Abstract
SUMMARY: Suicidal thinking, self-harm and suicidal acts are common, although determining their precise prevalence is complex. Epidemiological work has identified a number of associated demographic and clinical factors, though, with the exception of past acts of self-harm, these are non-specific and weak future predictors. There is a critical need shift focus from managing 'suicidality-by-proxy' through general mental health treatments, to better understand the neuropsychology and neurophysiology of such behaviour to guide targeted interventions. The model of the cognitive control of emotion (MCCE) offers such a paradigm, with an underlying pan-diagnostic pathophysiology of a hypoactive prefrontal cortex failing to suitably inhibit an overactive threat-responding limbic system. The result is a phenotype - from any number of causative gene-environment interactions - primed to impulsively self-harm. We argue that such neural dysconnectivity is open to potential therapeutic modification from repetitive transcranial magnetic stimulation (rTMS). The current evidence base for this is undoubtedly extremely limited, but the societal and clinical burden self-harm and suicide pose warrants such investigation. DECLARATION OF INTEREST: , but had no editorial involvement in the review or decision process regarding this paper. COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2015. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".