Evaluation of an apparatus to be combined with a smartphone for the early detection of spinal deformities
Bibliographic record
Abstract
BACKGROUND: Mobile smartphones are equipped with inclinometers enabling them to acquire angular clinical measures. The Scolioscreen has been developed in conjunction with a smartphone APP to enable the measure of the angle of trunk inclination (ATI) thus offering a convenient and reliable means to measure and screen for spinal deformities. The objective was to compare the reliability and accuracy of a Scolioscreen-smartphone combination, a smartphone alone, and a Scoliometer, for measuring the angle of trunk inclination in spinal deformities under blinded conditions for intra- and inter-observer analyses. METHODS: A cohort of 39 patients with adolescent idiopathic scoliosis were recruited. Each had maximum ATI measured by 3 observers: attending spine surgeon, nurse, and parent presenting with patient. Two series of measurements were performed by each observer using Scolioscreen-smartphone, smartphone alone and Scoliometer. Intra-class correlation coefficients (ICC) from two-way mixed model based on absolute agreement were used to assess intra- and inter-observer reliability as well as consistency between measurement techniques. RESULTS: Intra- and inter-observer reliability for measuring maximum ATI was 0.94-0.89 with Scolioscreen-smartphone, decreased to 0.89-0.75 for smartphone alone, and was 0.95- 0.89 for Scoliometer. Considering Scoliometer measurement taken by surgeon the gold standard, there was excellent consistency with measurements from Scolioscreen-smartphone taken by surgeon (ICC = 0.99), nurse (ICC = 0.95), and parent (ICC = 0.91). Conversely, consistency decreased when surgeon (ICC = 0.86), nurse (ICC = 0.86) and parent (ICC = 0.85) used smartphone alone. CONCLUSION: Study shows the Scolioscreen-smartphone to overcome limitations associated with ATI measurements using smartphones alone. The Scolioscreen-smartphone provides a reliability and consistency similar to the gold standard (use of Scoliometer by spine surgeon) and enables a parent to take reliable measurements on their own thus offering an accessible and convenient tool for all to use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".