Long-term follow-up of breast cancer survivors with post-mastectomy pain syndrome
Bibliographic record
Abstract
Post-mastectomy pain syndrome (PMPS) is a recognised complication of breast surgery although little is known about the long-term outcome of this chronic pain condition. In 1996, Smith et al identified a prevalence rate of PMPS of 43% among 408 women in the Grampian Region, Northeast Scotland. The aim of this study was to assess long-term outcome at 7-12 years postoperatively in this cohort of women, to describe the natural history of PMPS and impact of pain upon quality of life. Chronic pain and quality of life were assessed using the McGill Pain Questionnaire (MPQ) and Short Form-36 (SF-36). Of 175 women reporting PMPS in 1996, 138 were eligible for questionnaire follow-up in 2002. Mean time since surgery was 9 years (s.d. 1.8 years). A response rate of 82% (113 out of 138) was achieved; 59 out of 113 (52%) women reported continued PMPS and 54 out of 113 (48%) women reported their PMPS had resolved since the previous survey in 1996. Quality of life scores were significantly lower in women with persistent PMPS compared to those women whose pain had resolved. However, for women with persistent PMPS, SF-36 scores had improved over time. Risk factors for persistent PMPS included younger age and heavier weight. This study found that, of women reporting PMPS in 1996, half of those surveyed in 2002 continued to experience PMPS at a mean of 9 years after surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".