Exploring radiogrammetry beyond the second metacarpal: Using the third, fourth, and fifth metacarpals to quantify cortical bone
Bibliographic record
Abstract
OBJECTIVE: Traditional metacarpal radiogrammetry, a method for quantifying cortical bone in metacarpals to identify bone loss, typically relies on the presence of an unaltered or undamaged second metacarpal. This study compares the cortical indices of the second to the third, fourth, and fifth metacarpals to test if an additional metacarpal can be used as substitute when the second metacarpal is not available for study. METHODS: Hand and wrist radiographs from the Burlington Growth Study, belonging to 56 individuals (28 females; 28 males) between 18 and 20 years old, were included in this study. Cortical indices were calculated for metacarpals two through five. Cortical index differences were statistically compared by sex, and the second metacarpal cortical indices were correlated with those of the third, fourth, and fifth metacarpals. RESULTS: The third, fourth, and fifth metacarpal cortical indices were all significantly correlated with the second metacarpal cortical indices for both females and males (p < .05). Cortical indices of the second metacarpal were most strongly correlated with those of the third metacarpal (females r = .644, p < .001; males r = .643, p < .001). CONCLUSION: The results of this study indicate that the third, fourth, or fifth metacarpal may serve as substitutes for cortical index analyses when the second metacarpal is unavailable or unsuitable for analysis. While the second metacarpal should remain the primary choice in radiogrammetry analyses, the third metacarpal is the most suitable alternative for quantitative analyses of cortical bone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".