Pain After Breast Cancer Surgery is Predicted by Pre-Operative Immunological and Psychological Factors
Bibliographic record
Abstract
This study identified risk factors for pain intensity at rest and with movement, pain qualities and neuropathic pain 24 hours post-breast cancer surgery (BCS). Before surgery 86 women completed demographic, health status, and psychological questionnaires and blood was drawn to measure baseline cytokine levels. Numeric Rating Scale-Rest (NRS-R), NRS-Movement (NRS-M), Short-Form McGill Pain Questionnaire (SF-MPQ) and Short-Form Neuropathic Pain Questionnaire (SF-NPQ) were completed 24 hours post-BCS. Backward regression models found significant correlates for NRS-R: younger age, increased pain catastrophizing and bilateral surgery; NRS-M: younger age, increased trait anxiety, bilateral surgery, and mastectomy; SF-MPQ: increased pain catastrophizing, bilateral surgery, and previous breast surgery; and SF-NPQ: decreased interleukin-10 and increased pain catastrophizing. These results support the biopsychosocial model of pain and the importance of measuring multiple pain outcomes. Variables accounting for the most variance in each outcome (pain catastrophizing [NRS-R; SF-MPQ], trait anxiety [NRS-M] and baseline IL-10 [SF-NPQ]) are potentially modifiable.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".