Pilot Study of a Survey to Identify the Prevalence of and Risk Factors for Chronic Neuropathic Pain Following Breast Cancer Surgery
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To provide a preliminary determination of the prevalence rate of women who suffer from neuropathic pain post breast surgery (PPBS) and explore potential risk factors associated with its development. DESIGN: Prospective, quantitative, longitudinal survey. SETTING: Breast health clinic in western Canada. SAMPLE: A convenience sample of 17 women undergoing breast cancer surgery. METHODS: The Brief Pain Inventory was administered before surgery and 2 days, 10 days, and 3 months postsurgery. Demographic data also were collected preoperatively. Analysis included determining prevalence of PPBS; descriptive analyses on age, gender, and body mass index (BMI); presence of acute postoperative pain; type of surgery; and two-tailed t tests on age and BMI. MAIN RESEARCH VARIABLES: The symptom experience of chronic PPBS. FINDINGS: Twenty-three percent of the sample developed PPBS. Younger age (50 years or younger), more invasive surgery, acute postoperative pain, and less analgesic use during the acute postoperative period were factors associated with the development of PPBS. CONCLUSIONS: Additional research is required to confirm the significance of these potential risk factors in the development of PPBS. IMPLICATIONS FOR NURSING: Nurses are ideally situated to identify early signs of PPBS. In addition, nurses play a key role in the education of patients and healthcare professionals and can facilitate increased awareness about the possibility of developing PPBS, enabling earlier and more effective treatment of PPBS.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".