A Retrospective Study of Postmastectomy Pain Syndrome: Incidence, Characteristics, Risk Factors, and Influence on Quality of Life
Bibliographic record
Abstract
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 was mainly described as aching (62.9%). 144 women reported sensory disturbance. We found that only the younger age is the predictive factor for PMPS (P < 0.05). Compared to the patients who did not experience PMPS, those who suffered from PMPS had significantly worse scores in role limitations due to physical problems (role physical, RP), body pain (BP), general health (GH), vitality (VT), role limitations due to emotional problems (role emotional, RE), and mental health (MH) (P < 0.05). CONCLUSION: PMPS is a significant problem, and the possible risk factors should be further explored. Patients with PMPS have significant worse quality of life, suggesting that patients should be well informed about the likelihood of experiencing the pain, and they may be afforded greater predictability and higher perceived control to enhance their quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".