Pain-related Fear- From Different Fear Constructs to Dissociable Neural Sources
Bibliographic record
Abstract
The ability to infer emotional states through self-reports is often limited. Their measurement becomes even more challenging when considering emotional phenomena such as pain-related fear where different associated fear constructs have been proposed. Demonstrating significant predictive value regarding disability in patients with persistent musculoskeletal pain, pain-related fear is often assessed by questionnaires focusing on either fear of movement/(re)injury/kinesiophobia, fear avoidance beliefs or pain anxiety. Furthermore, the relationship of general anxiety measures such as trait anxiety to pain-related fear remains ambiguous. Advances in neuroimaging might help to support potential commonalities or differences across psychological constructs using appropriate machine learning techniques with the ability to reveal predictive relationships between neural information and questionnaire scores. Here, we applied a pattern regression approach using functional magnetic resonance imaging data of 20 non-specific chronic low back pain (LBP) patients. More specifically, we applied a novel approach using Multiple Kernel Learning that allows investigating the contribution of experimental conditions and regional neural information to a prediction model. We hypothesized to find evidence for or against a common fear construct by computing and comparing the prediction model of each questionnaire according to the contribution of fear-related neural information and conditions. The current results underpin the diversity of fear constructs among self-report measures of pain-related fear by demonstrating evidence of non-overlapping and differentially contributing neural sources within fear processing regions. Thus, the current approach might ultimately help to further understand and dissect the fear constructs captured by the various pain-related fear questionnaires.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".