Morphometric analysis of the patella and patellar ligament of South Africans of European ancestry
Bibliographic record
Abstract
Morphometric analyses of the patella and patellar ligament have been reported to be important in human identification, in knee implant design and in certain surgical procedures of the knee. It has also been shown that success in the functionality of a knee arthroplasty (knee replacement) is dependent on the implant being of an appropriate dimension. We undertook this study because of the lack of available data on these dimensions in South Africans. Careful dissection was carried out on both knees of 46 South African cadavers (25 females and 21 males) of European ancestry. The quadriceps femoris tendon and patellar ligament were carefully freed from the underlying structures. Eight measurements of the patella and patellar ligament were taken using a Vernier caliper. Patellae were also classified based on the dimensions of the articular facets. No significant difference was found when the measurements taken from both knees were compared except for the dimensions of patella thickness and widths. Dimensions of the patella, patellar ligament and articular facets are sexually dimorphic. In addition, measurements of the patella and patellar ligament showed significant positive correlations, with Type B patellae being the most prevalent in South Africans of European ancestry. The data from the present study will be beneficial in clinical and pathological practices and for local anthropological records.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".