The accuracy in determining pelvic tilt from anteroposterior pelvic radiographs in patients awaiting hip arthroplasty
Bibliographic record
Abstract
Spinopelvic mobility affects outcome after THA. Whether the sacro-femoral-pubic (SFP) angle, measured on AP radiographs, can be reliably used to estimate pelvic tilt (PT) in hip osteoarthritis patients is unknown. This study aimed to (1) validate the use of the SFP angle in the calculation of PT from AP radiographs, and (2) identify individual patient factors affecting the estimation of PT. A cohort of 100 patients awaiting THA for end-stage hip osteoarthritis was prospectively studied. AP and lateral radiographs, taken in the standing and relaxed-seated positions were evaluated for spinopelvic measurements (SFP, PT, and pelvic incidence [PI]). To validate the SFP angle, estimated PT values using the formula [PT = 75°-SFP] were compared to the true, measured values from the lateral radiographs. Despite good agreement for the estimated and true PT (16.2 ± 5.9° vs. 15.5 ± 8.6°; p = .315), a significantly poorer agreement could be found between the two methods at high or low values of PT. Patient-specific PI correlated with the difference between the two measurement methods (Pearson's r = -0.644; p < .001). However, the change in SFP angle equaled approximately the change in pelvic tilt (∆PT = 2°-∆SFP; Pearson's r = -0.934; p < .001). Absolute values for the sagittal PT should not be estimated from AP pelvic radiographs in patients awaiting total hip arthroplasty. However, the relative change in PT between different positions equals approximately the change in SFP angle. This may allow functional cup orientation after THA to be determined between different postures from an AP radiograph of the pelvis. The SFP angle has moderate accuracy in determining a patient's pelvic tilt; however, it can accurately determine a patient's change in pelvic tilt in different positions.
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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 | 0.018 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".