Predictors of Persistent Postsurgical Pain After Hysterectomy—A Prospective Cohort Study
Bibliographic record
Abstract
STUDY OBJECTIVE: To determine sociodemographic, surgical, and psychologic risk factors, including pain sensitivity, for persistent postsurgical pain (PPSP) after hysterectomy. DESIGN: A prospective cohort study. SETTING: Canadian academic medical center. PATIENTS: Patients (N = 200) who underwent hysterectomy (vaginal, laparoscopic, robotic, or open) between 2013 and 2014. INTERVENTIONS: Participants completed preoperative questionnaires assessing baseline pain scores and psychologic factors, including the Pain Sensitivity Questionnaire, Brief Pain Inventory Interference Items, the Beck Depression Inventory, the Numeric Rating Scale (NRS), and the Pain Catastrophizing Scale. Pain was recorded 1 and 24 hours postoperatively using the NRS. Patients were reassessed at 6 weeks postoperatively and completed the Brief Pain Inventory Interference Items, Patient Global Impression of Change, and the NRS. Patients who reported pain at 6 weeks were reassessed at 12 weeks using the above-mentioned questionnaires. MEASUREMENTS AND MAIN RESULTS: Of 200 study participants, 58 (32%) met the definition for PPSP (NRS ≥ 1 at 12 weeks), and 11 (6.1%) met the definition for moderate to severe postsurgical pain (NRS ≥ 4 at 12 weeks). Risk factors for PPSP included baseline pain scores, depression, pain catastrophizing, uterine mass, open surgical approach, acute postoperative pain, history of chronic pain, and having a hysterectomy due to pain. Multivariate regression analysis revealed that depression, pain catastrophizing, open surgical approach, and acute postoperative pain at 1 hour represent independent predictors of PPSP. Pain sensitivity was not associated with PPSP but was associated with acute and severe acute (NRS≥4) pain at 24 hours. CONCLUSION: Patients at risk for PPSP after hysterectomy can be identified preoperatively using validated questionnaires. This information can be used to guide targeted perioperative interventions to mitigate their risk.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".