Examination of the breast asymmetry associated with adolescent idiopathic scoliosis using surface topography methods
Bibliographic record
Abstract
Surface topography (ST) is a tool used to assess adolescent idiopathic scoliosis (AIS), which affects females more severely than males. However, current ST techniques fail to measure breast asymmetry related to torso deformity. Cosmetic deformity is important to patients because poor body appearance can cause psychological distress. Breast asymmetry is especially important to adolescent females. This study develops a method for assessing breast asymmetry using ST in patients with AIS, and proposes a reliable breast asymmetry classification for such patients. To achieve this, ST torso scans of 25 females (age: 15.4 ± 1.3 years; range: 13.5-17.5 years) with AIS were obtained. Scans were analyzed using a method that finds a rotoinversion matrix to minimize the distance between the torso scan and its reflected image about the sagittal plane. The mirrored torso was then fitted to the original torso using an iterative leastsquares method, such that the average deviation between the observed and reflected torsos was minimized. The relative deviation between the two torsos was measured and displayed as a deviation colour map (DCM). Each patient's DCM was visually appraised by two of the authors and a breast asymmetry classification system was created based on this appraisal. Good intra- and inter-rater reliability was found for the classification decisions by five observers. All of the patients presented breast asymmetry that could be reliably categorized into the proposed five-group classification with all patients exhibiting deviations exceeding the threshold of 3 mm between sides routinely observed in healthy teenagers without scoliosis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".