Polyethnic/Racial Differences in Sagittal Femoral Bowing: Clinical Implications
Bibliographic record
Abstract
Differences in sagittal femoral bowing (SFB) have implications for the survivability of total knee arthroplasty (TKA) prostheses. This quantitative study examined SFB anatomy of three distinct groups: an urban sample of contemporary European and African populations and an archaeological sample representing pre-European contact Ipiutak (500 BCE – 500 CE) and Tigara (1300 - 1700 CE) Inuit populations (significantly contrasting life styles and distinctive ethnic/racial affinities). Angular and linear measures of femurs were derived and intermedullary cavities documented by CT scanning. Length was measured from lesser trochanter to sagittal apex of lateral condyle. Quantity of curvature (CU) was measured from apex of shaft CU to proximal low point. Position of CU was measured from lesser trochanter to level of femoral CU apex. Quantity and position were indexed against femoral length to produce CU and position indices (CI, PI). Results of PI means were 0.43 (0.39–0.47), 0.29 (0.26–0.37), and 0.31 (0.28–0.33), for Inuit, Caucasian, and African samples, respectively. Mean PI for the Inuit sample was significantly different from both other groups (p<0.05) while mean CI for Inuit was different only from the African sample (p<0.05). Our preliminary findings suggest that polyethnic/racial differences in SFB be a consideration in clinical strategies for orthopaedic surgical management of TKA.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".