Distribution of epiphyseal nutrient foramina in the distal femur: Implications for anterior knee joint denervation
Bibliographic record
Abstract
Background: Sensory afferents supplying subchondral bone could mediate pain from the knee joint. Intrinsic innervation originates externally and follows blood vessels through nutrient foramina. Therefore, targeting the intrinsic innervation of subchondral bone can be achieved by capturing extrinsic innervation prior to their entry into the nutrient foramina. Understanding of extrinsic innervation of the knee joint as well as the distribution of the epiphyseal nutrient foramina are important. Currently, the distribution of nutrient foramina has not been analyzed. The objective of this osteological study was to quantify the distribution of nutrient foramina in the distal femur to inform knee joint denervation strategies. Methods: A convenience sample of 19 bony femurs was used in this study. The distal end of each specimen was photographed to obtain standardized lateral, medial, and anterior views. The location of nutrient foramina was documented. Each photograph was imported into ImageJ and the distribution of nutrient foramina was quantified. Results: Location of epiphyseal nutrient foramina was variable on distal femur. Laterally, distribution of nutrient foramina showed percentages of 11.5 %, 44.7 %, 36.5 %, and 7.3 % in the first, second, third, and fourth quadrants, respectively. Distribution on the medial distal femur showed percentages of 12.4 %, 40.4 %, 35.5 %, and 11.5 % in the first, second, third, and fourth quadrants, respectively. Anteriorly, distribution showed a difference between the medial and lateral halves with percentages of 71.1 % and 28.9 %, respectively. Conclusions: Epiphyseal nutrient foramina are important conduits that enable extrinsic innervation to enter and supply the subchondral bone. The location and distribution of the nutrient foramina of the distal femur reported in this study can be used to optimize nerve blocks and denervation techniques to manage chronic knee joint pain from osteoarthritis.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".