Modeling elemental strontium in human bone based on in-vivo x-ray fluorescence measurements in osteoporotic females self-supplementing with strontium citrate
Bibliographic record
Abstract
An in-house custom I-125 excited in vivo x-ray fluorescence (IVXRF) system was used to perform bone strontium (Sr) measurements in individuals suffering from osteoporosis and/or osteopenia. These individuals, who were self-administering with Sr supplements of their choice, were measured frequently, ranging from weekly to biweekly to monthly, over four years, as part of the Ryerson and McMaster Sr in Bone Research Study. Based on these data collected, data from eight subjects were used to perform kinetic modeling of Sr in human bone. Power and exponential models were used to model the data based on one and two compartmental systems. Model parameters included: mean normalized baseline bone Sr signal, half-life and bone Sr uptake. A one compartmental exponential model applied to finger and ankle bone measurements gave half-lives of (508 ± 331) d and (232 ± 183) d, respectively, but did not show statistically significant differences (p = 0.087 96). However, the values fall within literature estimates. When a two compartmental model was applied to finger bone measurements, half-lives of (300 ± 163) d and (2201 ± 1662) d were observed. Ankle bone data gave half-lives of (156 ± 117) d and (1681 ± 744) d. A two sample t-test, assuming unequal variances, showed these half-lives to be statistically different in both the finger and ankle bone measurements (p = 0.0147 and p = 0.00711, respectively). Common kinetic parameters amongst the different subjects could not be unambiguously identified due to the wide scatter of data, leading to an inconclusive kinetic model. The wide distribution of data is suggested to be physiological since technical and positioning factors were eliminated as possible causes. This outcome indicates the need for a more controlled study and further understanding of the physiological mechanism of Sr absorption.
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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 | 0.000 | 0.000 |
| 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".