The Reliability of Anthropometric Measurements Used Preoperatively in Aesthetic Breast Surgery
Bibliographic record
Abstract
BACKGROUND: Patient outcomes in aesthetic breast surgery are highly dependent on breast measurements used in preoperative planning. OBJECTIVES: The purpose of this study is to determine the reliability of anthropometric breast measurements. METHODS: Four raters measured 28 women using 7 measurements: sternal notch to nipple distance (Sn-N), nipple to midline (N-M), nipple to inframammary-fold distance under maximal stretch (N-IMF), breast base width (BW), soft tissue pinch thickness of the upper pole (STPT:UP), STPT at the inframammary fold (STPT:IMF), and anterior pull skin stretch (APSS). Reliability was assessed using intra-class correlation coefficients (ICCs). RESULTS: Inter-rater reliability was excellent for Sn-N, N-M, and BW (ICC = 0.94, 0.90, and 0.76, respectively) and was good for N-IMF (ICC = 0.70). The STPT:UP, STPT:IMF, and APSS measurements were not reliable between raters (ICC < 0.2). Intra-rater reliability was excellent for Sn-N, N-M, and BW for all raters (all ICC > 0.75). The N-IMF intra-rater reliability was excellent in senior raters (ICC > 0.75) and good in junior raters (ICC > 0.6). The STPT:UP, STPT:IMF, and APSS measurements showed fair or poor reliability for most raters (ICC < 0.6). CONCLUSIONS: The Sn-N, N-M, and BW measurements are very reliable. Dynamic measurements including APSS, STPT:UP, and STUP:IMF are unreliable. N-IMF is the only reliable dynamic measurement, and its reliability improves with increasing clinical experience. The variable reliability of preoperative measurements must be considered in the planning of aesthetic breast surgery. LEVEL OF EVIDENCE: 4 Diagnostic.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".