Measurement of Total Nickel in Body Fluids: Electrothermal Atomic Absorption Methods and Sources of Preanalytic Variation
Bibliographic record
Abstract
Before attempting to establish reference concentrations of Ni in blood serum or plasma, a laboratory must be working comfortably at the 2 nmol/L level. To achieve this, scrupulous acid-leaching of all materials that come into contact with the sample is essential, and blood collection with non-metalic systems is advisable. A particle-free environment (clean-air laboratory) seems necessary. Values of Ni in serum above 1.7 to 5.1 nmol/L are suspect; values in the urine of people without occupational or other unusual environmental exposures are an order of magnitude higher. For reported concentrations to be meaningful, the reference population must be thoroughly described in terms of age, sex, place of residence, occupation, diet, alcohol and tobacco use, medications and medical history, as all such factors may influence the actual concentrations of Ni. Sampling procedures must be standardized (for instance for time of day, season, posture of the person, etc.) and reported. Storage, if necessary, should minimize opportunities for sample adulteration, and these must be evaluated empirically. Preparation of samples should also minimize processing; acidification or protein precipitation with ultrapure nitric acid are adequate for clear body fluids. Analysis by ET-AAS with Zeeman correction is the most useful approach currently available.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".