Foot rotational effects on radiographic measures of lower limb alignment.
Bibliographic record
Abstract
INTRODUCTION: Surgical planning of high tibial osteotomy (HTO) typically requires the measurement of lower limb alignment from standing anteroposterior radiographs. Although every effort is made to maintain a standardized patient position, factors such as pain or anatomic constraints may necessitate acquiring the radiograph in a less than optimal patient position. One such constraint is natural rotation of the feet with respect to the tibia. The purpose of the present study was to investigate the magnitude of the effect of foot rotation on radiographic measures of lower limb alignment. METHODS: We analyzed 19 lower limbs from radiographs obtained from 10 people who reported to an orthopedic injuries clinic. Each patient was radiographed in 3 positions: 15 degrees of internal foot rotation, no foot rotation and 15 degrees of external foot rotation. We measured and compared the mechanical axis angle (hip-knee-ankle) and the mechanical axis deviation from each position. RESULTS: Compared with the position with no foot rotation, internal foot rotation resulted in less measured varus alignment and less mechanical axis deviation from the knee joint centre, whereas external foot rotation produced greater measured varus alignment and increased mechanical axis deviation from the knee joint centre. CONCLUSIONS: These results indicate that patient positioning is an important factor when measuring lower limb alignment from radiographs. As a result, special care must be taken when acquiring these radiographs for use in planning surgical procedures such as HTO.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".