Peripheral Quantitative Computed Tomography (pQCT) to Assess Bone Health in Children, Adolescents, and Young Adults
Bibliographic record
Abstract
The bone health of adults is dependent on the appropriate acquisition of peak bone mineral mass during late childhood and adolescence. Measurement of bone mineral density (BMD) in clinical practice is accomplished usually by dual energy x-ray absorptiometry that provides 2-dimensional (areal) values for BMD, does not distinguish cortical from trabecular bone, and should be adjusted for height and weight when used for children and adolescents. Peripheral quantitative computed tomography (pQCT) provides volumetric measures of BMD and geometry in both cortical and trabecular bone and, unlike dual energy x-ray absorptiometry, does not need adjustment for body size. Studies of bone health in young people require reference data for normative comparison. A literature review from 1946 to 2012 identified 1886 titles suggesting use of pQCT, with only 32 reporting some form of normative data. A detailed review of these 32 reports revealed a lack of consensus among users for standard scan locations in upper and lower limbs, acquisition protocols and analytical steps, and variables reported. Meaningful and effective use of pQCT for assessing bone strength and overall bone health in all age groups will require better defined normative data derived with common measuring techniques, equipment, and analytical approaches.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".