Appositional long bone growth: Implications for measuring cross‐sectional geometry
Bibliographic record
Abstract
Abstract Objectives As growth at the periosteal and endosteal surfaces varies with age, cross‐sectional geometric (CSG) properties derived from periosteal (“solid”) contours may not produce comparable results to those from endosteal and periosteal contours (“true”), contrary to findings from adults. Error in CSG properties derived from the “solid” sections is compared with “true” sections in a sample of archeologically derived skeletons with estimated dental ages ranging from 1.5 months to 23.5 years. Materials and Methods Cross sections were extracted from 3D surface models, and endosteal contours were located from biplanar radiographs for 56 femora and 59 humeri. Polar second moment of area (J), cross‐sectional shape (Imax/Imin), and polar section modulus (Zp) were calculated from solid and true sections. Relationships between solid and true properties were examined with least squares regression. Multiple regression examined the effect of age and % cortical area on solid section CSG error. Results While correlations were high (R2 = 0.72–0.99, all p < 0.001), the results indicate that solid CSG properties are not within an acceptable error range (%SEE of ≤8.0, and %PE of ≤5.0) of true CSG. Error was most affected by %CA, while estimated age was not correlated with %CA, %PE, or percent difference of true‐solid CSG. Discussion Periosteal contours alone should not be used to calculate CSG properties from individuals during the period of growth and development. Variation in bone growth and/or adaptive responses independent of age may account for the inconsistent age effects.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".