Characteristics of hyperparathyroid states in the Canadian multicentre osteoporosis study (CaMos) and relationship to skeletal markers
Bibliographic record
Abstract
CONTEXT: PTH is an essential regulator of mineral metabolism; PTH hypersecretion may result in hyperparathyroidism including normocalcaemic, primary and secondary hyperparathyroidism. OBJECTIVE: To examine the characteristics of participants with hyperparathyroid states and the relationship to bone mineral density (BMD). DESIGN AND PARTICIPANTS: A cross-sectional study of 1872 community-dwelling men and women aged 35+ years (mostly Caucasian) with available serum PTH from Year 10 Canadian Multicentre Osteoporosis Study follow-up (2005-07). PTH was determined using a second-generation chemiluminescence immunoassay. OUTCOME MEASURES: L1-L4, femoral neck and total hip BMD. RESULTS: We established a PTH reference range (2·7-10·2 pmol/l) based on healthy participants (i.e. normal serum calcium, serum 25-hydroxyvitamin D, kidney function and body mass index, who were nonusers of antiresorptives, glucocorticoids and diuretics and not diagnosed with diabetes or thyroid disease). Participants with PTH levels in the upper reference range (5·6-10·2 pmol/l), representing a prevalence of 10·7%, had lower femoral neck and total hip BMD, by 0·030 g/cm(2) [95% confidence interval: 0·009; 0·051] and 0·025 g/cm(2) (0·001; 0·049), respectively, than those with levels 2·7-5·6 pmol/l. Participants with normocalcaemic and secondary hyperparathyroidism also had lower total hip BMD than those with levels 2·7-5·6 pmol/l, and CaMos prevalences of normocalcaemic, primary and secondary hyperparathyroidism were 3·3%, 1·4% and 5·2%, respectively. CONCLUSION: We found reduced BMD in participants with accepted hyperparathyroid states but also a notable proportion of other participants that might benefit from having lower PTH levels.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".