Peer Review #1 of "Analysis of copper, selenium, and zinc in newborn dried bloodspots using total reflection X-ray fluorescence (TXRF) spectroscopy (v0.2)"
Bibliographic record
Abstract
Background.There exists great interest in using dried bloodspots across the clinical, public health, and nutritional sciences to characterize circulating levels of essential elements yet current methods face several challenges related to instrumentation, quality control, and matrix effects.Elemental analysis via total X-ray fluorescence (TXRF) may help overcome these challenges.The objective of this study was to develop and apply a novel TXRF-based analytical method to quantify essential elements (copper, selenium, zinc) in dried bloodspots.Methods.Analytical methods were developed with human whole blood standard reference materials from the Institut National de Santé Publique du Québec (INSPQ).The method was developed in careful consideration of several quality control parameters (e.g., analytical accuracy, precision, linearity, and assay range) which were iteratively investigated to help refine and realize a robust method.The developed method was then applied to a quantitative descriptive survey of punches (n=675) taken from residual dried bloodspots from a newborn screening biobank program (Michigan BioTrust for Health).Results.The analytical method developed to quantify the 3 target elements in dried bloodspots fared well against a priori quality control criteria (i.e., analytical accuracy, precision, linearity and range).In applying this new method, the average (± SD) blood copper, selenium, and zinc levels in the newborn samples were 1,117.0± 627.1 g/L, 193.1 ± 49.1 g/L, and 4,485 ± 2,275 g/L respectively.All the elements were normally distributed in the sample population, and the measured concentrations fall within an expected range.Conclusions.This study developed and applied a novel and robust method to simultaneously quantify three essential elements.The method helps overcome challenges in the field concerning
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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 | 0.015 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.301 | 0.229 |
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".