Peer Review #2 of "Reference values for bone density and bone mineral content from 5 to 80 years old in a province of Chile (v0.1)"
Bibliographic record
Abstract
Background: The assessment of bone health throughout the life cycle is essential to determine fracture risk.The objectives of the work were a) compare bone mineral density and content with international references from the United States, b) determine maximum bone mass, c) propose references for bone health measurements from age 5 to 80 years old.Methods: Research was carried out on 5,416 subjects.Weight and height were measured.Body Mass Index (BMI) was calculated.The total body was scanned using dual energy X-ray absorptiometry (DXA).Information was extracted from the bone health measures [Bone mineral density (BMD) and bone mineral content (BMC)] for both sexes, according to pediatric and adult software.Results and Discussion: Differences were identified between the mean values of Chilean and American men for BMD (~0.03 to 0.11 g/cm2) and BMC (~0.15 to 0.46 g).The Chilean females showed average values for BMD similar to the US references (~ -0.01 to 0.02 g/cm2).At the same time, they were relatively higher for BMC (~ 0.07 to 0.33g).The cubic polynomial regression model reflected a relationship between BMD and BMC with chronological age in both sexes.For males, R 2 was higher (R 2 = 0.72 and 0.75) than for females (R 2 = 0.59 and 0.66).The estimate of maximum Bone Mass (MBM) for males emerged at 30 years old (1.45±0.18g/cm 2 of BMD and 3.57±0.60g of BMC) and for females at age 28 (1.22±0.13g/cm 2 of BMD and 2.57±0.44g of BMC).The LMS technique was used to generate smoothed percentiles for BMD and BMC by age and sex.Results showed that maximum bone mass occurred in PeerJ reviewing
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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 | 0.013 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.325 | 0.215 |
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".