The Hip-spine Assessment of a Novel Surgical Planning Software Provides Acetabular Component Targets That Are Reliable and in Agreement With Current Clinical Recommendations
Bibliographic record
Abstract
Background: Spinopelvic immobility has been reported to increase dislocation risk following total hip arthroplasty. Surgically placing acetabular components in a functional orientation has been shown to mitigate risk. The aim of this study was to evaluate the validity and reliability of novel surgical planning software to generate clinically recommended cup targets. Methods: Hip-spine assessments were performed retrospectively on 40 patients. Five reviewers, including 3 arthroplasty-trained surgical fellows and 2 clinical research scientists performed the assessments. Hip-spine assessments consisted of measuring anterior pelvic plane tilt, sacral slope, pelvic incidence, and lumbar lordosis on standing anteroposterior pelvis and lateral standing and seated hip-spine images. Generated cup targets and a control group (40°/20° relative to the anterior pelvic plane) were compared to clinically recommended cup targets. Agreement was defined as a cup position within the recommended range or within 3° of a specific target (eg, 40° inclination) when no range was provided. Intraclass correlation coefficients were used to assess interrater and intrarater reliability, and McNemar's chi-square test was used to measure success relative to the control group. Results: The intraclass correlation coefficient was 0.88 for delta sacral slope and 0.92 for pelvic incidence-lumbar lordosis mismatch. For patients with spinopelvic risk factors, the generated targets matched the clinical recommendations in 81% of patients compared to only 16% in the control group. Conclusions: Excellent interrater and intrarater reliability was achieved using the novel surgical planning software. The resultant target values agreed with clinical recommendations to a greater extent than the control group.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".