Evidence map›Paper›PMID 41623429›Full record

ArticleAesthetic surgery journal. Open forum2026

The Anatomical Breast Burden Model: A Schnur Scale Alternative for Identifying Need for Therapeutic Reduction Mammaplasty.

Eliana Jolkovsky, Meghan N Miller, Weston McClain, Samantha Rabinovich, Ainaz Dory Barkhordarzadeh, Derrick Lin, Stacy Piva, Addee Lerner, Ginger C Slack

Abstract read
In one paragraph

Article in Aesthetic surgery journal. Open forum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Eliana Jolkovsky
Meghan N Miller
Weston McClain
Samantha Rabinovich
Ainaz Dory Barkhordarzadeh
Derrick Lin
Stacy Piva
Addee Lerner
Ginger C Slack

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Schnur sliding scale is used by many insurance companies to determine eligibility for reduction mammaplasty. However, it overlooks anatomical features, symptoms, and physical findings, leading to inequities in coverage determinations. The aim of the study was to develop and validate the anatomical breast burden (ABB) model-a 0-6-point scoring system that quantifies the burden of breasts using breast measurements, physical findings, and symptoms-as an alternative tool to the Schnur scale. A retrospective chart review was conducted for 84 patients who underwent breast reduction at a single academic center. Resection weights, Schnur threshold weights, demographics, and preoperative breast measurements were recorded. ABB cutoff values for sternal notch-to-nipple, nipple-to-inframammary fold, and base width distances were determined statistically, and a score was assigned to each patient. Spearman correlation tested the associations between ABB score and BMI, body surface area, breast measurements, ptosis, resection weight, and the discrepancy between actual resection weight and the Schnur threshold weight. Among patients with an ABB score of 6 (very severe burden), 44% were ineligible for coverage under the Schnur scale, whereas 33% of patients with an ABB score of 2 (mild-to-moderate burden) were eligible. When stratified as low (0-2) and high (3-6) burden, the Schnur scale showed 47.5% sensitivity and 66.7% specificity for identifying patients with significant breast burden. Our model demonstrated stronger correlations with anatomical measurements and resection weight than the Schnur scale. The ABB model better reflects true breast burden and offers a more equitable tool for guiding coverage decisions in breast reduction.

Identifiers

PMID41623429
PMCPMC12853872

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.