The Effects of Breast Reduction on Back Pain and Spine Measurements: A Systematic Review
Bibliographic record
Abstract
The aim of this review article was to synthesize the literature on reduction mammaplasty and its effects on the spine. The particular focus was to find these few radiological studies and those investigating changes in spinal angles, posture, center of gravity, and back pain reduction. METHODS: We performed a thorough review of the literature, searching the Medline database for all relevant published data studying reduction mammaplasty and the spine. The search yielded 107 articles of which 11 articles met our specific inclusion criteria. The primary outcome measures of the studies and their respective results were tabulated, contrasted, and compared. RESULTS: The 11 cohort studies included in this review cover the period from 2005 to 2015 and focus on breast hypertrophy and spine. According to these 11 quantitative studies, breast hypertrophy causes objective, quantitative, measurable disturbances to women living with this condition. Reduction mammaplasty produces an unmistakable improvement in signs, symptoms, and quantifiable measures. Although the majority of included articles in this review described postoperative improvement in spinal angles, there remain discrepancies of results between them. CONCLUSIONS: The studies included in this review did offer a promising glimpse into the complex interaction between breast hypertrophy and the spine. However, future research initiatives can improve upon what these investigators have begun with more refined, objective, radiological evidence. More specifically, we aim to clarify some of the basic hypotheses in our center with the use of EOS.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".