Fuzzy-logic–assisted Surgical Planning in Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
SUMMARY OF BACKGROUND DATA: Selection of appropriate curve fusion levels for surgery in adolescent idiopathic scoliosis (AIS) is a complex and difficult task and, despite numerous publications, still remains a highly controversial topic. OBJECTIVE: To evaluate a fuzzy-logic-based surgical planning tool by comparing the results suggested by the software with the average outcome recommended by a panel of 5 expert spinal deformity surgeons. It is hypothesized that, given the same information, the fuzzy-logic tool will perform as favorably as the surgeons. STUDY DESIGN: Proof-of-concept study evaluating the use of a fuzzy-logic-assisted surgical planning tool in AIS to select the appropriate spinal curve to be instrumented. METHODS: A cohort of 30 AIS surgical cases with a main thoracic curve was used. Each case included standard measurements recorded from preoperative standing postero-anterior and lateral, supine side bending, and 1-year postoperative standing radiographs. Five experienced spinal deformity surgeons evaluated each case independently and gave their preferred levels of instrumentation and fusion. The cases were then presented to the fuzzy-logic tool to determine whether the high thoracic and/or lumbar curves were to be instrumented. For each case, a percentage value was obtained indicating inclusion/exclusion of the respective curves in the surgical instrumentation procedure. Kappa statistics was used to compare the model output and the average decision of the surgeons. RESULTS: Kappa values of 0.71 and 0.64 were obtained, respectively, for the proximal thoracic and lumbar curves models, thus suggesting a good agreement of the fusion recommendations made by the fuzzy-logic tool and the surgeons. CONCLUSIONS: Given the same information, the fuzzy-logic-assisted recommendation of the curve to be instrumented compared favorably with the collective decision of the surgeons. The findings thus suggest that a fuzzy-logic approach is helpful in assisting surgeons with the preoperative selection of curve instrumentation and fusion levels in AIS.
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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) | 0.001 | 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 | 0.001 |
| 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".