A test of two methods of radiographically deriving long bone cross‐sectional properties compared to direct sectioning of the diaphysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Numerous studies have made use of cross‐sectional geometry to describe the distribution of cortical bone in long bone diaphyses. Several methods can be used to measure or estimate cross‐sectional contours. Direct sectioning (DSM) of the diaphysis is not appropriate in most curatorial contexts, and is commonly substituted with methods based upon bi‐planar radiography: a latex cast method (LCM) or an eccentric elliptical method (EEM). Previous studies have demonstrated that the EEM provides accurate estimates of area measurements, while providing less accurate estimates of second moments of area (Biknevicius & Ruff, 1992 ; Runestad et al. , 1993 ; Lazenby, 1997 ). The LCM has been commonly employed, as a way to estimate section contours more accurately, yet the validity of this method has not been adequately documented. This study measures the agreement of these methods against DSM of long bone diaphyses using 21 sections of canine tibiae derived from a study of total hip arthroplasty. The accuracy and agreement of these methods is evaluated using reduced major axis regression, paired sample t‐tests and tests for agreement (Bland & Altman, 1986). The results illustrate that the LCM provides a reasonable estimate of cross‐sectional dimensions, producing cross‐sectional properties that are on average within 5% of properties derived from the DSM. The EEM is found to provide adequate estimates of true cross‐sectional areas, but poor estimates of second moments of area. The use of the LCM is supported for all cross‐sectional properties, but the EEM is only accurate in total area, cortical area and percent cortical area estimates. Copyright © 2002 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it