Healthcare Use for Pain in Women Waiting for Gynaecological Surgery
Bibliographic record
Abstract
Background. Pain while waiting for surgery may increase healthcare utilization (HCU) preoperatively. Objective. Examine the association between preoperative pain and HCU in the year prior to gynecological surgery. Methods. 590 women waiting for surgery in a Canadian tertiary care centre were asked to report on HCU in the year before surgery. Pain was assessed using the Brief Pain Inventory. Results. 33% reported moderate to severe pain intensity and interference in the week before surgery. Sixty-one percent (n = 360) reported a total of 2026 healthcare visits, with 21% (n = 126) reporting six or more visits in the year before surgery. After controlling for covariates, women with moderate to severe (>3/10) pain intensity/interference reported higher odds of overall HCU (≥3 pain-related visits to family doctor or specialist in the past year or ≥1 to emergency/walk-in clinic) compared to women with no or mild pain. Lower body mass index (BMI < 30 versus ≥30) and anxiety and/or depression were associated with emergency department or walk-in visits but not visits to family doctors or specialists. Conclusions. There is a high burden of pain in women awaiting gynecological surgery. Decisions about resource allocation should consider the impact of pain on individuals and the healthcare system.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".