Bibliographic record
Abstract
Phosphate binding drugs are crucial in reducing blood phosphate concentrations in individuals with end-stage renal disease (ESRD) to prevent hyperphosphatemia. While many phosphate binding drugs exist, lanthanum carbonate [La2(CO3)3, LaC] has been reported to be better tolerated than previously used calcium- and aluminum-based phosphate binders. However, since large doses ranging up to 3750 mg LaC/day is orally administered to ESRD patients, concerns have been raised as to whether chronic exposure to lanthanum (La) may lead to La toxicity on bone health. In this thesis, a robust x-ray fluorescence (XRF) detection system was developed and can be potentially used to non-invasively measure La in ESRD patients. The first step in testing the feasibility of the system was by measuring La in a series of bone mimicking hydroxyapatite phantoms doped with known concentrations of La. The sensitivity of the detection system was evaluated by investigating the minimum detection limit of the system. Initially a 90° irradiation-detection geometry was utilized but was deemed inadequate for in vivo measurements due to challenges in experimental reproducibility. Therefore, a 180° backscatter irradiation-detection geometry was adopted for all subsequent measurements. Since it is anticipated that the developed XRF system will be used clinically to measure bone La concentrations in the ESRD population who are administered LaC, interpatient variability such as amount of overlying tissue thickness at the measurement site, bone size and subject-source movement will affect the intensity of the La x-ray signal. Therefore, a coherent normalization correction procedure was investigated to determine whether these factors could be accounted to allow the true value of the bone La concentration to be estimated during in vivo measurements involving living subjects. In addition, a dosimetry study was performed to determine the radiation dose delivered to a participant during a 2000 s XRF measurement. Lastly, since literature data on the natural accumulation of La in bone from the environment and diet is limited, an ex vivo study was performed on 10 excised human cadavers obtained from deceased donors with no previous exposure to La. Based on these studies, this dissertation presents the development of an XRF system that is robust, safe and has the potential to determine in vivo bone La concentrations in the ESRD population who are orally administered LaC.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".