Use of backscattered scanning electron microscopy to quantify the bone tissues of mid‐thoracic human ribs
Bibliographic record
Abstract
Abstract Objectives Novel information on apartheid health conditions may be obtained through the study of recent skeletal collections. Using a backscattered scanning electron microscopy (BSE‐SEM) approach, this study aims to produce bone quality and tissue mineralization data for an understudied South African population from the Western Cape province. Methods Using BSE‐SEM imaging, cortical porosity (Ct.Po), osteocyte lacunar density (Ot.Lc.Dn), and the degree of tissue mineralization were quantified in mid‐thoracic ribs from the Kirsten Skeletal Collection. Individuals ( nfemale = 75, nmale = 68, and mean age = 46.3 years) were predominantly from the South Africa Colored (SAC) population group ( nSAC = 103, 72%). Full cross‐sectional images of each rib were manually stitched together in Adobe Photoshop. Photomontages were imported into MATALB (Mathworks, Natick, MA) for image processing and analysis. Age‐related changes in histomorphometric parameters and sex differences were examined using correlation analysis, as well as linear and nonlinear regressions. Results Young adult men have significantly less mineralized bone and fewer osteocyte lacunae, compared to women. Only men demonstrate a significant negative relationship between Ot.Lc.Dn and age. Average tissue mineralization decreases with age in women, while Ct.Po increases. Pore area (Po.Ar) does not vary with age, but pore density (Po.Dn) is highest in the perimenopause, when accelerated rates of bone turnover are first anticipated. Ct.Po is highest in the years following the predicted age of menopause, but levels off in the final decades of life. Conclusions Men and women display disparate patterns of bone aging. Systemic disenfranchisement of non‐white population groups affected bone health in South Africa, and may continue to do so today. Indicators of poor bone quality are evident in the full study sample, indicating that osteoporosis and fracture risk are not just of concern to the aged white female population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".