A test of two methods of radiographically deriving long bone cross‐sectional properties compared to direct sectioning of the diaphysis
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.073 | 0.263 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".