The role of excess subcutaneous fat in pain and sensory sensitivity in obesity
Bibliographic record
Abstract
BACKGROUND: Previous studies suggest pain sensitivity may be decreased in obesity, but it is unknown whether this is a global or a site-specific phenomenon related to the amount of excess fat. DESIGN: a cross-sectional study comparing obese and non-obese participants on body sites with much and little excess subcutaneous fat in obesity. Hot and cold sensory detection thresholds, pain thresholds, pain tolerance and subjective ratings for a cold (0 °C) and hot (48 °C) stimulus were assessed using a 16 × 16 mm thermode (Medoc, Israel) on the forehead and abdomen. Pressure pain thresholds were measured on the hand. Cold water immersion tolerance duration and subjective ratings were assessed on the hand. Two indices of central pain processing, i.e., temporal summation and heterotopic noxious stimulation, were assessed. RESULTS: A total of 20 obese participants [10M/10F, BMI mean (SD) =41.5 kg/m(2) (9.4 kg/m(2) )] and 20 age- and gender-matched non-obese controls [10M/10F, BMI mean (SD) =23.5 kg/m(2) (2.9 kg/m(2) )] were studied. Compared with non-obese, obese participants had higher thresholds and lower subjective ratings, indexing decreased sensitivity, for painful and non-painful thermal stimuli on the abdomen, an area with much excess subcutaneous fat. Decreases in abdominal sensitivity correlated with measures of adiposity (i.e., waist-to-hip ratio and subcutaneous fat thickness). On areas with little excess subcutaneous fat (forehead and hand), obese and non-obese groups did not differ in measures of thermal or pressure sensitivity, nor for indices of central pain processing. CONCLUSION: Obese participants are less sensitive than non-obese individuals, but only on areas with excess subcutaneous fat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".