Does expecting more pain make it more intense? Factors associated with the first week pain trajectories after breast cancer surgery
Bibliographic record
Abstract
The aim of this study was to identify clinical risk factors for unfavorable pain trajectories after breast cancer surgery, to better understand the association between pain expectation, psychological distress, and acute postoperative pain. This prospective study included 563 women treated for breast cancer. Psychological data included questionnaires for depressive symptoms and anxiety. Experimental pain tests for heat and cold were performed before surgery. The amount of oxycodone needed for satisfactory pain relief after surgery was recorded. Pain intensity in the area of operation before surgery and during the first postoperative week and expected intensity of postoperative pain were recorded using the Numerical Rating Scale (NRS 0-10). Pain trajectories were formed to describe both initial intensity (the intercept) and the direction of the pain path (the slope). Factors associated with higher initial pain intensity (the intercept) were the amount of oxycodone needed for adequate analgesia, psychological distress, type of axillary surgery, preoperative pain in the area of the operation, and expectation of postoperative pain. The higher the pain initially was, the faster it resolved over the week. Expectation of severe postoperative pain was associated with higher scores of both experimental and clinical pain intensity and psychological factors. The results confirm that acute pain after breast cancer surgery is a multidimensional phenomenon. Psychological distress, pain expectation, and the patients' report of preoperative pain in the area to be operated should be recognized before surgery. Patients having axillary clearance need more efficient analgesic approaches.
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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 | 0.003 |
| 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) | 0.001 | 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".