Neuropathic pain after thoracotomy: Tracking signs and symptoms before and at monthly intervals following surgery
Bibliographic record
Abstract
BACKGROUND: As the development of neuropathic symptoms contributes to pain severity and chronification after surgery, their early prediction is important to allow targeted treatment. OBJECTIVES: We longitudinally investigated trajectories of signs and symptoms in patients undergoing thoracotomy and assessed whether and at which time they were related to the development of neuropathic pain symptoms 6 months after surgery. METHODS: Presurgical and 6 monthly postsurgical assessments included questionnaires for mental and physical well-being (e.g., depression/anxiety, pain catastrophizing, sleep quality, neuropathic pain symptoms), and quantitative sensory testing (QST). RESULTS: QST trajectories indicated nerve impairment of the surgery site with predominant loss of function. Signs of recovery towards the end of the assessment period were observed for some tests. Unsupervised cluster analysis with NPSI scores 6 months after surgery as clustering variable identified one group with no/low levels of neuropathic symptoms and one with moderate levels. The two groups differed w.r.t. several signs and symptoms already at early time points. Notably, neuropathic pain anywhere in the body differed already preoperatively and sleep impairment differentiated the two groups at all time points. Regression analysis revealed three factors that seemed particularly suited to predicted 6 months NPSI scores, namely preoperative neuropathic pain symptoms, with contributions from sleep impairment 1 month after surgery and the presence of dynamic mechanical allodynia 3 months after surgery. CONCLUSIONS: Clinical routine should focus on the individual's physiological state, including pre-existing neuropathic pain and sleep quality to identify patients early who might be at risk to develop chronic post-surgical neuropathic pain. SIGNIFICANCE: Development of neuropathies contributes to pain severity and pain chronification after surgery. Here we demonstrate trajectories of quantitative sensory tests (assessed at monthly intervals for 6 months after surgery) that reveal accurate time courses of gain/loss of nerve function following thoracotomy. Independent of the degree of neuropathic signs after surgery, the main predictors for post-surgical neuropathic pain are self-reported neuropathic pain before surgery and sleep quality shortly after surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".