An emergency radiobioassay method for 226Ra in human urine samples
Bibliographic record
Abstract
A new radioanalytical method was developed for rapid determination of (226)Ra in human urine samples. The method is based on organic removal and decolourisation of a urine sample by a polymeric (acrylic ester) solid phase sorbent material followed by extraction and preconcentration of (226)Ra in an organic solvent using a dispersive liquid-liquid microextraction technique. Radiometric measurement of (226)Ra was carried out using a liquid scintillation counting instrument. The minimum detectable activity for the method (0.15 Bq l(-1)) is lower than the required sensitivity of 0.2 Bq l(-1) for (226)Ra in human urine samples as defined in the requirements for radiation emergency bioassay techniques for the public and first responders based on the dose threshold for possible medical attention recommended by the International Commission on Radiological Protection (ICRP). The accuracy (expressed as relative bias, B(r)) and repeatability of the method (expressed as relative precision, S(B)) evaluated at the reference level (2 Bq l(-1)) were found to be -4.5 and 2.6 %, respectively. The sample turnaround time was <5 h for a single urine sample and <20 h for a batch of six urine samples. With the fast sample turnaround time combined with the potential to carry out the analysis in a field deployable mobile laboratory, the newly developed method can be used for emergency radiobioassay of (226)Ra in human urine samples following a radiological or nuclear accident.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".