How Does Volume of Resection Relate to Symptom Relief for Reduction Mammaplasty Patients?
Bibliographic record
Abstract
BACKGROUND: Reduction mammaplasty surgery is well known to produce improvement in a wide range of symptoms associated with macromastia. Health care insurers frequently stipulate a minimum resection volume to qualify for coverage, limiting access to surgery for many. The authors aimed to identify whether small volume resections do produce symptomatic improvement, comparing preoperative and postoperative experience of symptoms across a range of tissue resection volumes. METHODS: Reduction mammaplasty patients were given a custom-designed questionnaire at routine postoperative follow-up appointments, asking them to rate their preoperative and postoperative experience of 9 symptoms related to macromastia. Results were compiled and analyzed alongside data from patient case notes. Of 661 patients identified as being eligible for inclusion in the study, 410 had sufficiently complete data to proceed to statistical analysis. Patients were divided into 6 groups based on volume of breast tissue resected. A Schnur sliding scale percentile was also calculated for all patients. Statistical analysis of preoperative symptom prevalence and postoperative symptom change was carried out. Further analysis to examine for evidence of trend in symptom improvement across groups was implemented using the Jonckheere-Terpstra test for ordered alternatives. RESULTS: Patients who go on to have larger volumes of breast tissue resected were found to experience back pain, shoulder grooves, breast pain, rashes under the breast, exercise intolerance, and poor posture more frequently than those who go on to have smaller resections (P < 0.0005 for all). However, across the range of resection volumes, preoperatively symptomatic patients experienced significant improvement in several symptoms. Results suggested that a larger resection volume may correspond with greater improvement in back pain, neck pain, and poor posture. CONCLUSIONS: We found that reduction mammaplasty has a positive impact on a range of symptoms, even with lower volume resections and regardless of body surface area-calculated adjustments. This adds further weight to the argument that patients should not be denied access to the surgery based on arbitrary volume restrictions. We advocate freedom for the surgeon to make a decision on potential benefits of surgery based around the needs of each individual patient.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".