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Record W4317477987 · doi:10.1093/asj/sjad012

Commentary on: Patient-Reported Outcomes After Reduction Mammoplasty Using BREAST-Q: A Systematic Review and Meta-Analysis

2023· review· en· W4317477987 on OpenAlexaffabout
Elizabeth J. Hall-Findlay

Bibliographic record

VenueAesthetic Surgery Journal · 2023
Typereview
Languageen
FieldMedicine
TopicBreast Implant and Reconstruction
Canadian institutionsBanff Centre
Fundersnot available
KeywordsMedicineReduction MammoplastyMammoplastyBreast reductionMeta-analysisGeneral surgeryGerontologySurgeryInternal medicineMammaplastyBreast cancer

Abstract

fetched live from OpenAlex

See the Original Article here. This is a helpful compilation of 28 qualified papers which have tried to sort out what variables result in more or less patient satisfaction with breast reduction as assessed by the validated BREAST-Q questionnaire.1 We are all aware that breast reduction is one of the most satisfying procedures in plastic surgery. The BREAST-Q, developed by Pusic et al in 2009, has allowed us to better evaluate what factors lead to more or less patient satisfaction in both physical and psychosocial aspects of patient well-being.2 The most important information provided by this review is that the amount of tissue resected is unrelated to patient satisfaction. Before validated questionnaires were available, I had patients fill out Likert scales comparing preoperative and postoperative symptoms after breast reduction, and Strong then performed a statistical analysis which showed that symptom improvement was independent of the amount of breast tissue resected.3 The authors conclude that these papers support a reassessment of insurance criteria for breast reduction coverage. Small breast reductions had high satisfaction scores. We all know that the Schnur scale was never meant to be used by insurance companies and it is unhelpful in determining when a breast reduction should be considered cosmetic vs medically indicated.4 I disagree with the authors’ words that a breast reduction is “medically necessary,” and I would prefer the description “medically indicated.” But when should psychosocial satisfaction be the criteria for coverage vs physical symptom improvement? We all know that patients who request purely cosmetic procedures such as mastopexy or breast augmentation can have significant improvement in psychosocial parameters, but should they be covered by insurance? As one of my Canadian colleagues frequently pointed out, patients want a breast reduction for medical reasons, but they sue us for the cosmetic result (John Taylor, personal communication, April 1994). The main conclusions from this meta-analysis are that the papers with large numbers of patients—Persichetti's group from Italy (414 patients),5 Jorgensen's group from Denmark (393 patients),6 and Cabral's group from Brazil (107 patients)7—show that not only is overall patient satisfaction in multiple areas significantly improved, but satisfaction rates are slightly reduced with higher BMIs and complication rates, and are increased with increasing age. Resection amounts did not affect satisfaction rates. We can also learn from series such as that presented by Morris et al that looked at racial disparities in 115 patients.8 It is clear from their review that we still have a lot to learn and improve in that aspect of our practices. Ozbey et al claim that “patient satisfaction is the most important determinant of surgical success,” but I would argue that we should somehow also be looking at aesthetic improvement from a surgical standpoint.9 We do not yet have a good way of analyzing the aesthetic result and this is where the BREAST-Q is not going to be particularly helpful. We know that our management of patient expectations can play a huge role in patient satisfaction. We have all experienced patients who are very satisfied with results that we find less than optimal from a surgical aesthetic perspective. The authors sometimes got sidetracked from patient satisfaction questionnaires and comment on irrelevant aspects of surgical technique. For example, they note that Aquinati et al's paper showed a “validated use of the hammock flaps for breast reduction resulting in positive outcomes in patient reported satisfaction.”10 There were only 10 patients in that study with no before-and-after proof that the hammock flaps had anything to do with either improved results or patient satisfaction. In fact, Aquinati et al only include 2 postoperative photographs with no preoperative photograph for comparison. Overall, however, this is an excellent review of validated studies based on the BREAST-Q that will help us understand which aspects of our breast reduction candidates will affect patient satisfaction scores. The author declared no potential conflicts of interest with respect to the research, authorship, and publication of this article. The author received no financial support for the research, authorship, and publication of this article.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.129
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.028
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.129
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0040.004
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0050.002
Research integrity0.0170.010
Insufficient payload (model declined to judge)0.0280.009

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.

Opus teacher head0.105
GPT teacher head0.339
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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