Pain threshold and pain tolerance in young people with self-injurious behavior: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Pain sensitivity has been proposed as a contributing factor to self-injurious behavior (SIB). Meta-analytic results show that individuals with SIB have lower pain sensitivity than healthy controls (HC). However, these findings are primarily based on adult populations. SIB typically begins in the early teen years and is most prevalent among youth. The aim of the present meta-analysis was to quantify the association of SIB and pain thresholds and pain tolerance in young people aged 10 to 24 years. METHODS: We performed a systematic search of the literature (MEDLINE, Web of Science Core Collection and PsycINFO) up until 10 December 2024. Titles, abstracts, and full texts were independently screened by multiple reviewers. Random-effects meta-analysis was performed on two pain-related outcomes: pain threshold and pain tolerance. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed. Quality assessment was performed using the Newcastle-Ottawa scale. RESULTS: Of 5200 screened studies, 221 full-text articles were retrieved whereof 8 studies fulfilled the criteria (n=592). Participants ranged from 10 to 22 years. Meta-analysis demonstrated statistically significantly higher pain threshold (Hedges' g = 0.79, 95 % CI [0.13, 1.46]) in individuals with SIB compared to HC and no statistically significant difference in pain tolerance (Hedges' g = 0.39, 95 % CI [-0.02; 0.79], p = 0.056). CONCLUSIONS: Young people with SIB demonstrate higher pain thresholds compared to healthy controls, suggesting that lower sensitivity to painful stimulation may be a risk factor for SIB across developmental stages. Future studies should examine whether this association is independent of psychiatric comorbidity and other confounding factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".