Reduction mammoplasty improves body posture and decreases the perception of pain
Bibliographic record
Abstract
Women with hypertrophic breasts often experience body pain and posture problems, which tend to be reduced or even eliminated after reduction mammoplasty. The present study aimed to analyze the effects of reduction mammoplasty on anthropometric variables, body posture and pain in women with breast hypertrophy. Eleven women (mean [± SD] age 31.3±10.4 years) participated in the present study. Anthropometric variables, body posture and pain perception were evaluated pretest, and 60 (post60) and 90 (post90) days after reduction mammoplasty. Commercially available posture analysis software was used to analyze the following variables: acromial horizontal alignment (AHA), angle between acromial and anterior superior iliac spines (A-AAIS), vertical alignment of right (R) and left (L) trunk (VAT), vertical alignment of R and L body (VAB) and horizontal alignment of R and L pelvis (HAP). Descriptive statistics and ANOVA for repeated measures were used, and effect sizes (ES) were measured; the level of significance was set at P<0.05. There were no significant differences in anthropometric variables among the assessments. Only HAP-R showed a significant decrease; however, when analyzed, ES, VAT- L and HAP- L in post60, and VAT-R, VAT-L, HAP-R, HAP-L and VAB-L in post90 showed large ES after mammoplasty (ES>0.70). There were significant reductions in pain at post60 and post90 in the neck, cervical spine, back, shoulder and arm (P<0.05). Following mammoplasty, an improvement in body posture, primarily in the alignment of shoulders, trunk and pelvis, and a decrease in pain in the upper limbs and spine, were observed.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".