Research Article A Retrospective Study of Postmastectomy Pain Syndrome: Incidence, Characteristics, Risk Factors, and Influence on Quality of Life
Bibliographic record
Abstract
Copyright © 2013 Yang Meijuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Objective. The underlying cause for postmastectomy pain syndrome (PMPS) and its impact on quality of life remain unclear. The objective of this study aims to determine retrospectively the prevalence of PMPS, its predicting risk factors, and its impact on quality of life.Method. In this survey, 225 women completed a battery of questionnaires. The questionnaires comprised the short form of the McGill Pain Questionnaire (SF-MPQ) exploring the characteristics and the description of the pain, and a Short Form-36 (SF-36) Health Survey evaluating quality of life. Logistic regression analyses were subsequently performed to identify risk factors for PMPS. Results. 62 women (27.6%) reported PMPS as a consequence of surgery, and the pain was generally mild, mostly localized in breast area and intermittent.The pain wasmainly described as aching (62.9%). 144 women reported sensory disturbance.We found that only the younger age is the predictive factor for PMPS (
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".