A longitudinal study of pain, personality, and brain plasticity following peripheral nerve injury
Bibliographic record
Abstract
We do not know precisely why pain develops and becomes chronic after peripheral nerve injury (PNI), but it is likely due to biological and psychological factors. Here, we tested the hypotheses that (1) high Pain Catastrophizing Scale (PCS) scores at the time of injury and repair are associated with pain and cold sensitivity after 1-year recovery and (2) insula gray matter changes reflect the course of injury and improvements over time. Ten patients with complete median and/or ulnar nerve transections and surgical repair were tested ∼3 weeks after surgical nerve repair (time 1) and ∼1 year later for 6 of the 10 patients (time 2). Patients and 10 age-/sex-matched healthy controls completed questionnaires that assessed pain (patients) and personality and underwent quantitative sensory testing and 3T MRI to assess cortical thickness. In patients, pain intensity and neuropathic pain correlated with pain catastrophizing. Time 1 pain catastrophizing trended toward predicting cold pain thresholds at time 2, and at time 1 cortical thickness of the right insula was reduced. At time 2, chronic pain was related to the time 1 pain-PCS relationship and cold sensitivity, pain catastrophizing correlated with cold pain threshold, and insula thickness reversed to control levels. This study highlights the interplay between personality, sensory function, and pain in patients following PNI and repair. The PCS-pain association suggests that a focus on affective or negative components of pain could render patients vulnerable to chronic pain. Cold sensitivity and structural insula changes may reflect altered thermosensory or sensorimotor awareness representations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".