A Machine Learning Approach to Assess Changes in Scoliosis
Bibliographic record
Abstract
This paper presents a machine learning approach that can be used to evaluate the validity of the results obtained with an automated system to measure changes in scoliotic curves. The automated system was used to measure the inclinations of 141 vertebral endplates in spine radiographs of patients with scoliosis. The resulting dataset was divided into training and test set. The training set was used to configure three classifiers: a support vector classifier (SVC), a decision tree classifier (DT) and a logistic regression classifier (LR). Their performance was evaluated on the test set. The SVC had an accuracy of 86% discriminating Good Results (those in which the error was less than 3 degrees ) from Bad Results. This accuracy was better than that of the LR (76%) and DT (68%). The differentiation between Good and Bad Results using the proposed machine learning approach was achieved successfully.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".