A Dangerous Curve: Impact of the COVID-19 Pandemic on Brace Treatment in Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
Study Design Observational Cohort study. Objectives We aim to document the abandon and irregular compliance rate towards brace treatment during the COVID-19 pandemic and its impact on AIS progression. Methods We reviewed a database of AIS patients recruited between March and September 2020. We included AIS patients under brace treatment according to SRS criteria. The patients were divided in 2 cohorts: those with self-reported Good-Compliance (GC) to treatment and those who had a Bad-Compliance (BC). Data analysis included biometric and radiographic data at first visit and last follow-up and percentage of progression. Unpaired student-t tests and Chi 2 were used for comparison. Results 152 patients met inclusion criteria. 89 patients (age:12.1y.o.±1.4) reported good adherence to treatment, while 63 patients (age:12.7y.o.±1.8) were not compliant. Within the BC group, 18 patients reported irregular brace wear, while 45 had completely abandoned treatment (abandon rate of 29%). The GC cohort started treatment with a mean main thoracic (MT) curve of 26° and finished with 27°. The mean difference between measurements was +.65°±7.5; mean progression rate was -4.6%. However, the BC cohort started with a mean MT curve of 27° and finished with 32°, with a mean increase of +5°±8 and a mean progression rate of -13%. The differences between the 2 cohorts were statistically significant ( P = .0002). Six patients from the BC group progressed and were offered surgery. Conclusion The abandon rate of brace treatment in AIS significantly increased during the first wave of COVID-19 pandemic. Patients who voluntarily discontinued treatment had significant increases in curve progression and surgical indication rates. Level of evidence III
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".