Assessing Changes in Three Dimensional Scoliotic Deformities with Difference Maps
Bibliographic record
Abstract
Topographical difference maps were used to compare the trunk surfaces of subjects over the course of their treatment. Three-dimensional points representing the trunk surfaces were aligned accounting for growth and positioning. A goodness-of-fit score was calculated and a color map used to display trunk surface changes. Fifty-one successive subjects were assessed with difference maps. Two subjects each had 10 repetitions taken on the same day to assess reliability. A blinded observer used a five-point scale that extended from full agreement to full disagrment to judge the maps according to the extent and location of changes. The observations were compared to clinical measures mapped onto the same scale by another blinded observer. Goodness of fit for repeated measures averaged 5 +/- 1, for subjects deemed to have no change 7 +/- 2, for subjects with slight change 9 +/- 2, and 14 +/- 2 for subjects with significant change. Judges were in full agreement or in agreemnt with forty of the fifty-one subjects (78%) and in slight disagreement with the remaining eleven. When the cohort was subdivided in surgical, brace and no treatment groups, the judges were in full agreement or in agreement 76%, 80%, and 85% respectively. The difference map provides a qualitative and quantitative measure of how the trunk surface has changed as a whole.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".