Examining the Neural Basis of Pain Tolerance and Fearlessness About Death in Suicide Risk: A Research Protocol
Bibliographic record
Abstract
Introduction: Suicide is a global health concern that takes the lives of over 700,000 people each year. Suicide capability – including heightened pain tolerance and fearlessness about death – may explain the progression from suicidal ideation to a suicide attempt. Thus, investigating the neural circuitry associated with pain, fear, and suicide risk presents a unique opportunity to identify biomarkers of suicide capability and contribute to our understanding of the transition from suicidal ideation to suicide attempt. Methods: A total of 90 adults aged 18 to 65 will be recruited from Toronto, Canada. Participants will either be patients with current suicidal ideation but no previous suicide attempt (n=30), patients with current suicidal ideation and a suicide attempt within the past six months (n=30), or healthy controls (n=30). Participants will complete self-report measures and magnetic resonance imaging tasks measuring pain tolerance and fearlessness about death. Results: We expect that suicide attempters will exhibit significantly higher pain tolerance and fearlessness about death than suicide ideators or healthy controls. We also predict negative associations between self-reported suicide capability and pain- and fear-related neural activation. Discussion: Findings of the present study may contribute to the validation of ideation-to-action models of suicide by providing neurobiological evidence supporting the distinction between suicide ideators and attempters. Conclusion: By examining the neural underpinnings of suicide capability, our work contributes to the understanding of biomarkers indicating those at greatest risk of suicide.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.011 |
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".