Evaluation of a novel portable x-ray fluorescence screening tool for detection of arsenic exposure
Bibliographic record
Abstract
A new portable x-ray fluorescence (XRF) screening tool was evaluated for its effectiveness in arsenic (As) quantification in human finger and toe nails ([Formula: see text]). Nail samples were measured for total As concentration by XRF and inductively coupled plasma-mass spectrometry (ICP-MS). Using concordance correlation coefficient (CCC), kappa, diagnostic sensitivity (Sn) and specificity (Sp), and linear regression analyses, the concentration of As measured by XRF was compared to ICP-MS. The CCC peaked for scaled values of fingernail samples, at 0.424 (95% CI: 0.065-0.784). The largest kappa value, 0.400 (95% CI: -0.282-1.000), was found at a 1.3 μg g(-1) cut-off concentration, for fingernails only, and the largest kappa at a clinically relevant cut-off concentration of 1.0 μg g(-1) was 0.237 (95% CI: -0.068-0.543), again in fingernails. Analyses generally showed excellent XRF Sn (up to 100%, 95% CI: 48-100%), but low Sp (up to 30% for the same analysis, 95% CI: 14-50%). Portable XRF shows some potential for use as a screening tool with fingernail samples. The difference between XRF and ICP-MS measurements decreased as sample mass increased to 30 mg. While this novel method of As detection in nails has shown relatively high agreement in some scenarios, this portable XRF is not currently considered suitable as a substitute for ICP-MS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".