Cross-sectional Versus Longitudinal Change in a Prospective HR-pQCT Study
Bibliographic record
Abstract
Longitudinal studies assessing age-related changes using high-resolution peripheral quantitative computed tomography (HR-pQCT) provide novel insight compared with cross-sectional analyses. The purpose of this cohort study was 1) to determine individuals' change in HR-pQCT parameters over 5 years relative to least significant change (LSC), and 2) to evaluate if predicted rate of change from cross-sectional data is comparable to actual change from longitudinal investigation. A cohort of 466 (162 male, 304 female) participants completed two HR-pQCT scans with 5 years between assessments. After image registration, standard and cortical morphological analyses were conducted. Rate of bone microarchitectural change was compared between cross-sectional models and actual change calculated from longitudinal analyses. At the young end of the life span, we observed gains in total bone density of +0.2% to +2.9% per year, whereas the older participants (aged >50 years) lost total bone density at a rate of -0.3% to -1.3% per year. Declines in total bone density begin at age 40 years in females and 60 years in males, and significant adaptation was found at both ends of the age spectrum with respect to the LSC. Models predicting rate of change from cross-sectional data were similar to the actual change reported in this longitudinal study for total density and cortical thickness at the radius and cortical density at the tibia, but we found that changes in comparison to our 5-year longitudinal results were often overestimated from cross-sectional data. Studies aimed at observing age-related changes in a normative cohort, especially in a follow-up period of less than 5 years, are better to focus on the tibia rather than the radius because of the increased sensitivity to change at the tibia. © 2017 American Society for Bone and Mineral Research.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".