Breast Hypertrophy: A Real Pain in the Back
Bibliographic record
Abstract
BACKGROUND: Bilateral breast hypertrophy comes with signs and symptoms ranging from mild to debilitating. Bilateral breast reduction (BBR) is one of the most frequently performed plastic surgery procedures, and its effects on parameters such as spinal balance, paraspinal muscle function, and physical performance have not been thoroughly evaluated. The objective of this study was to evaluate the effects of BBR using advanced spine imaging modalities, and pain resolution. METHODS: A prospective, observational, cohort study was carried out at the McGill University Health Centre. The following measures were recorded preoperatively and postoperatively for each patient: patient questionnaires (BREAST-Q and Pain), magnetic resonance imaging, and EOS low-radiation spinal scan. RESULTS: Significant postoperative pain reduction was recorded, and there was up to 148% improvement in physical tests. Improvement in all questionnaire and BREAST-Q categories was documented. Preoperative and postoperative magnetic resonance imaging did demonstrate a statistically significant absence of permanent anatomical skeletal sequelae. Postoperative improvement in thoracic kyphosis was documented. CONCLUSIONS: Quality-of-life scores are uniformly improved following BBR. Key findings following BBR include significant pain reduction and no evidence of spinal skeletal change. This is a finding of major importance in view of the practice of many insurance companies/third-party payer and health care systems that use the Schnur scale. The Schnur scale associates a weight for resection with body size that is not directly predictive of pain relief. This may indicate the need for more precise or different guidelines based on these quantitative findings. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".