Three-dimensional True Spine Length: A Novel Technique for Assessing the Outcomes of Scoliosis Surgery
Bibliographic record
Abstract
BACKGROUND: Current assessment of spine growth for patients undergoing growth friendly surgical treatment for early-onset scoliosis (EOS) is the use of serial, 1-dimensional standard-of-care coronal vertical spine height (SoCVH) measurements. Any growth of the spine out of the coronal plane is missed by the SoCVH, which may underestimate the actual growth of the spine. This study set to validate the novel 3-dimensional true spine length (3DTSL) radiographic measurement technique for measuring growth of EOS patients. METHODS: 3DTSL measurement accuracy, reliability, and repeatability was assessed using 10 physical model configurations. In addition, interrater and intrarater reliabilities (IRRs) were assessed using interclass coefficient (ICC) analyses of 23 retrospective EOS patient clinical radiographs. 3DTSL measurements were compared with SoCVH measurements. RESULTS: The model assessment showed excellent accuracy with a mean error of 1.2 mm (SD=0.9; range, 0.0 to 3.0) and mean ICC of 0.999.IRR ICCs of the clinical radiographs averaged 0.952 for the 3DTSL and 0.975 for the vertical height whereas IRRs averaged 0.944 and 0.965, respectively (all P<0.001).Mean clinical 3DTSL curve lengths were 193.9 mm (SD=30.0; range, 142.8 to 276.8), whereas the SoCVH averaged 156.1 mm (SD=29.7; range, 74.7 to 207.3). The mean difference between the matched 3DTSL and SoCVH measurements was 37.8 mm (SD=21.4; range, 1.3 to 95.4) and was statistically significant (P<0.0001). On average, the 3DTSL of the measured spines was 124.2% of the measured SoCVH, with a progressive difference as the Cobb or kyphosis angles increased. CONCLUSIONS: The novel 3DTSL measurement is accurate, repeatable, and complements the current growth assessments for EOS treatments. LEVEL OF EVIDENCE: Level II-diagnostic study-development of a diagnostic criteria on basis of consecutive patients, with gold standard.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".