Sensory cognitive abnormalities of pain in autism spectrum disorder: a case–control study
Bibliographic record
Abstract
BACKGROUND: The Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) recently included sensory processing abnormalities in the diagnostic criteria for individuals with autism spectrum disorder (ASD). However, there is no standard method for evaluating sensory abnormalities in individuals with ASD. METHODS: Fifteen individuals with ASD and 15 age- and sex-matched controls were enrolled in this study. We compared objective pain sensitivity by measuring the pain detection threshold and pain tolerance to three different stimuli (electricity, heat, and cold). Then, we compared both subjective pain sensitivity, assessed by the visual analog scale (VAS), and quality of pain, assessed by the short-form McGill Pain Questionnaire (SF-MPQ), to determine the maximum tolerable pain intensities of each stimulation. RESULTS: The pain detection threshold and pain tolerance of individuals with ASD were not impaired, indicating that there were no differences in the somatic perception of pain between groups. However, individuals with ASD were hyposensitive to subjective pain intensity compared to controls (VAS; electrical: p = 0.044, cold: p = 0.011, heat: p = 0.042) and hyposensitive to affective aspects of pain sensitivity (SF-MPQ; electrical: p = 0.0071, cold: p = 0.042). CONCLUSIONS: Our results suggest that the cognitive pathways for pain processing are impaired in ASD and, furthermore, that our methodology can be used to assess pain sensitivity in individuals with ASD. Further investigations into sensory abnormalities in individuals with ASD are needed to clarify the pathophysiologic processes that may alter sensory processing in this disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".