The Effects of Breast Reduction on Back Pain and Spine Measurements: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it