Discriminative and Predictive Validity of the Scoliosis Research Society-22 Questionnaire in Management and Curve-Severity Subgroups of Adolescents With Idiopathic Scoliosis
Bibliographic record
Abstract
STUDY DESIGN: Prospective cross-sectional measurement study. OBJECTIVE: To determine the ability of the Scoliosis Research Society (SRS)-22 questionnaire to discriminate among management and scoliosis severity subgroups and to correlate with internal and external measures of curve severity. SUMMARY OF BACKGROUND DATA: In earlier studies of the SRS-22 discriminative ability, age was not a controlled factor. The ability of the SRS-22 to predict curve severity has not been thoroughly examined. METHODS: The SRS-22 was completed by 227 females with adolescent idiopathic scoliosis. Using Analysis of covariance analyses controlling for age, the SRS-22 scores were compared among management subgroups (observation, brace, presurgery, and postsurgery) and curve-severity subgroups (in nonoperated subjects: Cobb angles of <30 degrees, 30 degrees -50 degrees, and >50 degrees). A stepwise discriminant analysis was used to identify the SRS-22 domains most discriminative for curve-severity categories. Correlation between SRS-22 scores and radiographic or surface topography measurements was used to determine the predictive ability of the questionnaire. RESULTS: Pain was better for subjects treated with braces than for those planning surgery. Self-image was better for subjects under observation or postsurgery than for those planning surgery. Satisfaction was better for the brace and postsurgery subgroups than for the observation or presurgery subgroups. Statistically significant mean differences between subgroups were all larger than 0.5, which is within the range of minimal clinically important differences recommended for each of the 5-point SRS-22 domain scoring scales. Pain and mental health were worse for those with Cobb angles of >50 degrees than with Cobb angles of 30 degrees to 50 degrees. Self-image and total scores were worse for those with Cobb angles of >50 degrees than both other subgroups. Using discriminant analysis, self-image was the only SRS-22 domain score selected to classify subjects within curve severity subgroups. The percentage of patients accurately classified was 54% when trying to classify within 3 curve severity subgroups. The percentage of patients accurately classified was 73% when classifying simply as those with curves larger or smaller than 50 degrees . CONCLUSION: Pain, self-image, and satisfaction scores could discriminate among management subgroups, but function, mental health and total scores could not. The total score and all domain scores except satisfaction discriminated among curve-severity subgroups. Using discriminant analysis, self-image was the only domain retained in a model predicting curve-severity categories.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".