Bibliographic record
Abstract
BACKGROUND AND PURPOSE: People in persistent pain have been reported to pay increased attention to specific words or descriptors of pain. The amount of attention paid to pain or cues for pain (such as pain descriptors), has been shown to be a major factor in the modulation of persistent pain. This relationship suggests the possibility that language may have a role both in understanding and managing the persistent pain experience. The aim of this paper is to describe current models of neuromatrices for pain and language, consider the role of attention in persistent pain states and highlight discrepancies, in previous studies based on the McGill Pain Questionnaire (MPQ), of the role of attention on pain descriptors. The existence of a pain neuromatrix originally proposed by Melzack (1990) has been supported by emerging technologies. Similar technologies have recently allowed identification of multiple areas of involvement for the processing of auditory input and the construction of language. As with the construction of pain, this neuromatrix for speech and language may intersect with neural systems for broader cognitive functions such as attention, memory and emotion. METHOD: A systematic search was undertaken to identify experimental or review studies, which specifically investigated the role of attention on pain descriptors (as cues for pain) in persistent pain patients. A total of 99 articles were retrieved from six databases, with 66 articles meeting the inclusion criteria. After duplicated articles were eliminated, the remaining 41 articles were reviewed in order to support a link between persistent pain, pain descriptors and attention. RESULTS: This review revealed a diverse range of specific pain descriptors, the majority of which were derived from the MPQ. Increased attention to pain descriptors was consistently reported to be associated with emotional state as well as being a significant factor in maintaining persistent pain. However, attempts to investigate the attentional bias of specific pain descriptors highlighted discrepancies between the studies. As well as the diversity of pain descriptors used in studies, they were inconsistently categorized into domains of pain. A lack of consistent bias towards certain pain descriptors was observed, and may be explained simply by the fact that the words provided are not those which subjects themselves would use. CONCLUSION: These findings suggest that the multidimensional and individual nature of the persistent pain experience may not be adequately explained by pain questionnaires such as the MPQ. Personalized pain descriptors may communicate the pain experience more appropriately, but may also contribute to an increased sensitivity of cortical pain processing areas by capturing increased attention for that individual. The language used as part of communication between therapists and people with persistent pain may provide an, as yet, unexplored adjunct strategy in management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".