Pain intensity and pain affect in relation to white matter changes
Bibliographic record
Abstract
Since aging is a risk factor for both dementia and the occurrence of painful conditions, with the number of aged people increasing in the next decades, an increase in the number of elderly people suffering from both conditions can be anticipated. Reliable pain assessment in this population is restricted by reduced communicative and cognitive capacity, with serious consequences for effective pain treatment. White matter changes are frequently observed in the various subtypes of dementia as well as in normal aging, and may play a crucial role in pain processing. In healthy elderly people, reliable pain assessment can be accomplished, which enables examining the relationship between pain experience and white matter changes. A normal structure and function of the white matter is extremely important for dorsolateral prefrontal cortex (DLPFC) functioning, which has recently been linked to pain inhibition. The present study focused on the relation between white matter changes and both pain intensity and pain affect in elderly people without dementia. The Coloured Analogue Scale (CAS) and the Number of Words Chosen-Affective (NWC-A) were applied to measure pain intensity and pain affect, respectively. The presence of white matter changes was significantly related to a higher score on the NWC-A but not the CAS score. These results suggest that pain experience may change as a result of aging and that white matter changes might be indicative for these alterations.
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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 | 0.000 | 0.003 |
| 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".