Can Future Back Pain in AIS Subjects be Predicted during Adolescence from the Severity of the Deformity?
Bibliographic record
Abstract
Back pain is frequently reported as a symptom of adolescent idiopathic scoliosis (AIS). Prediction of pain in adulthood would be useful to identify subjects requiring follow-up. The aim is to determine adolescent predictors of adult back pain. This study is a retrospective review of 27 females with AIS who attended our pediatric scoliosis clinic and later completed the SRS-22 questionnaire as young adults (range 18-25 years). Subjects with surgery at baseline (age 14-16 years) were excluded. The relationships between largest curve size, decompensation and trunk twist at baseline and pain as measured by the SRS-22 pain domain as young adults were studied. At baseline, subjects had a largest curve of 47+/-15 degrees , decompensation of 18+/-14 mm and trunk twist of 14+/-6 degrees . At follow-up, 5.3+/-1.9 years later, the total SRS-22 score was 3.9+/-0.3 and the pain domain score was 3.9+/-0.7. Pearson correlations between the SRS-22 pain domain and largest curve, decompensation and trunk twist were 0.17, -0.11 and -0.25, respectively (p>0.05). Individual questions within the pain domain had similar correlations. Even though the sample represented a wide range of scoliosis severity at baseline and a wide range of pain scores (2.4 to 5) at follow-up, baseline scoliosis deformity parameters of largest curve size, decompensation and trunk twist did not predict scoliosis-related pain in young adulthood.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".