Pain threshold and pain tolerance as a predictor of deliberate self-harm among adolescents and young adults
Bibliographic record
Abstract
Sir, Miglani et al.[1] have evaluated pain sensitivity in those with nonsuicidal self-injury, suicide attempters, and healthy controls and found higher pain intensities and pain threshold in the first group compared to the other two. The study findings are interesting, but we have some concerns regarding the methodology. The age of participants varied from 10 to 25 years in those with suicide attempt and those with nonsuicidal self-injury, whereas healthy controls included age and gender-matched caregivers of the patients, which appears unlikely. Studies suggest that as age advances, pain threshold and pain tolerance increase.[2] Furthermore, it is not clear why those with a history of previous suicide attempts were excluded, which can result in a biased sample. For the assessment of pain, the McGill pain questionnaire-short form was used, which is a subjective measure. Although McGill Pain Questionnaire - Short Form has been used in adolescents, its use in children may not be appropriate,[3] It is not clear whether English version of the questionnaire was used or it was translated to local language and validated before use. The sample characteristics of the three groups is not mentioned, which affects generalizability of the findings. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".