Validity and Reliability of the iPhone to Measure Rib Hump in Scoliosis
Bibliographic record
Abstract
STUDY DESIGN: This was a prospective blinded validity and reliability analysis. OBJECTIVE: The aim of this study was validation and reliability evaluation of the Scoligauge iPhone app. BACKGROUND: The scoliometer is used to clinically measure the rib hump in scoliosis as a means to evaluate the axial trunk rotation. The increasing availability of smartphone with built-in accelerometer led to the development of a vast number of applications to measure angles. Of these, the Scoligauge mimics a scoliometer. The aim of this study was to compare the validity of the Scoligauge iPhone application without an associated adapter with the traditional scoliometer and to test the reliability of the application in a clinical setting. METHODS: Two observers measured the rib hump deformity on 34 consecutive patients with idiopathic scoliosis with an average Cobb angle of 24.2 ± 13.5 degrees (range, 4 to 65 degrees). Measurements were made with an iPhone without the adapter and with a scoliometer. The validity as well as the interobserver and intraobserver reliability were calculated using the intraclass coefficient (ICC) and the Bland-Altman test. RESULTS: The mean difference between the scoliometer and the Scoligauge application was 0.4 degrees [95% confidence interval (CI) of ± 3.1 degrees] with an ICC of 0.947 (P < 0.001). The intraobserver and interobserver ICC were 0.961 (P < 0.001) and 0.901 (P < 0.001), respectively. The mean intraobserver difference was 0.0 degrees (95% CI of ± 2.7 degrees) and the mean interobserver difference was 0.1 degrees (95% CI of ± 4.4 degrees). CONCLUSIONS: The intraobserver and interobserver reliability of the Scoligauge iPhone app, as well as its validity compared with the scoliometer, are excellent. The mean differences between measurements are small and clinically not significant. Thus, the Scoligauge application is valid for clinical evaluation even without special adapter. LEVEL OF EVIDENCE: Level I (Diagnostic Study).
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".