Spine Curve Measures Taken in Individuals with Osteoporosis and Osteopenia Using the IONmed Mobile Phone Application Are Highly Consistent with Digital Inclinometer Measures
Bibliographic record
Abstract
Introduction. This study determined the agreement between measures of standing posture in individuals at risk of osteoporotic fracture obtained using the IONmed mobile phone application and a digital inclinometer. Methods. One experienced rater assessed 20 adults, aged 55–88 years, attending an outpatient osteoporosis clinic. A standardized protocol was duplicated to acquire a single measure of spine curvature (lumbosacral angle, lordosis, kyphosis) using the IONmed mobile phone application and a digital inclinometer. Interdevice agreement was determined using the intraclass correlation coefficient (ICC) with 95% confidence intervals (CI) and standard error of the measurement (SEM) with 95% CI and Bland-Altman plots. Results. ICC (95% CI) for lumbosacral angle, lordosis, and kyphosis is 0.97 (0.92, 0.99), 0.97 (0.92, 0.99), and 0.99 (0.98, 1.0), respectively. SEM (95% CI) for lumbosacral angle, lordosis, and kyphosis is 0.99° (0.75, 1.44), 1.33° (1.01, 1.94), and 1.23° (0.94, 1.80), respectively. Measurement bias is small. Conclusions. In our sample, the IONmed mobile phone application provides measures highly consistent with the digital inclinometer measures of standing posture. This method offers clinicians a quick, convenient way of identifying alterations in spine alignment which require followup and remediation.
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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 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".