Factors associated with acute postoperative pain following breast reconstruction
Bibliographic record
Abstract
INTRODUCTION: Post-mastectomy breast reconstruction has become an increasingly important component of breast cancer treatment. Unfortunately, some patients experience severe postoperative pain, placing them at risk for increased clinical morbidity and the development of disabling chronic pain. In an attempt to identify at-risk patients, we prospectively evaluated patient characteristics and medical/surgical variables associated with more severe acute post-reconstruction pain. METHODS: Women (N = 2207; one-week 82.8% response rate) undergoing breast reconstruction were assessed for pain experience, anxiety, depression, and sociodemographic characteristics prior to surgery. Pain assessments were made preoperatively and postoperative at 1-week using validated survey instruments including the McGill Pain Questionnaire-Short Form (MPQ-SF), Numerical Pain Rating Scale (NPRS), and BREAST-Q Chest and Upper Body scale. Depressive symptoms and anxiety severity were assessed by the Patient Health Questionnaire and Generalized Anxiety Disorders Scale, respectively. Mixed-effects regression modeling was used to examine the relationships between patient characteristics and medical/surgical factors and 1-week postoperative pain. RESULTS: Younger age, bilateral reconstruction, and severity of preoperative pain, anxiety and depression were all associated with more severe acute postoperative pain on all the pain measures and BREAST-Q. Comparison of surgical procedure type indicated less severe postoperative pain for PTRAM, DIEP and SIEA reconstructive surgery compared to tissue expander/implant reconstruction. CONCLUSIONS: This study identified patients at risk for greater acute postoperative pain following breast reconstruction. These findings will allow plastic surgeons to better tailor postoperative care to improve patient comfort, reduce clinical morbidity, and further enhance patient satisfaction with their surgical outcome.
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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) | 0.004 | 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".