Bibliographic record
Abstract
Background and aimLower limb malalignment is a major risk factor for knee osteoarthritis (OA) and is mainly diagnosed using the Hip Knee Ankle Angle (HKA).Therefore, accurate HKA measurements are indispensable.We aimed to study the effects of knee flexion, leg rotation, and X-ray beam height on the accuracy of the HKA measurement.We aimed to convert our findings into a guideline for obtaining whole leg radiographs (WLR) in favour of accuracy and reproducibility. MethodsAn in vitro experiment was designed using sawbones (in 5° varus) of the whole lower limb, fixated in different leg rotation angles, knee flexion angles, HKAs and three different X-ray beam heights. ResultsThe HKA measurement error was 1° per 20° of leg rotation without flexion (p<0.01).When 5° of flexion was added, the HKA measurement error was 0.8° per 20° rotation (p<0.01).When the leg was in 15° flexion, the HKA measurement error was 4° per 20° rotation (p<0.01).X-ray beam height did not cause any significant measurement errors (p=0.348). ConclusionThis study showed that leg rotation only can lead to clinically relevant measurement errors when exceeding 9°.When there is 15° of knee flexion and leg rotation the error becomes approximately 3°.Varying X-ray beam heights within a range of 10 cm does not affect the accuracy.Based on these findings, we propose guidelines for system setup and patient positioning during a WLR that is easy to apply and aims at minimizing errors when measuring the HKA. Figure 1.Measurement of the Hip-Knee-Ankle angle on full limb radiographThe Hip-Knee-Ankle angle (HKAA, in green) is measured between two axes (in red).One axis runs from the middle of the femoral head to the middle of the femoral notch, and a second axis from the middle of the tibial notch, to the middle of the talar head.Chapter 4
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.095 | 0.050 |
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".