High prevalence of elevated molybdenum levels in pediatric CKD patients. A cross-sectional and longitudinal study
Bibliographic record
Abstract
AIMS: Many of the secondary effects of high levels of molybdenum (Mo) overlap with symptoms commonly seen in pediatric patients with chronic kidney disease (CKD). We measured plasma Mo levels and examined the relationship between Mo levels and kidney function. MATERIALS AND METHODS: The study was carried out at the London Health Sciences Centre in London, Ontario, Canada with 36 children and adolescents 4 - 18 years of age with CKD. There were 1 - 6 trace element measurements (Mo and copper (Cu)) per patient. We studied the proportion of patients with abnormal trace element levels and the relationship between trace element levels and estimated glomerular filtration rate (eGFR), calculated using the Filler formula. Plasma Mo and Cu levels were measured using High Resolution Sector Field Inductively Coupled Mass Spectrometry. Anthropomorphic data and blood parameters were collected from our electronic chart program. RESULTS: (35, 75). Median Mo level was 2.00 µg/L (1.40, 2.88). 20 patients had at least one set of Mo levels above the published reference interval in either unit, and the results of 46% of the tests were above the interval. There was a strong negative correlation between the Mo levels and the eGFR (Spearman's r = -0.627, p < 0.0001). CONCLUSIONS: Our study suggests that pediatric patients with CKD have elevated plasma levels of Mo, which may cause secondary effects commonly associated with CKD. The elevated Mo levels in our center's catchment area may cause an accumulation of this trace element in patients with impaired renal function. .
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".