Optimisation of nasal swab analysis by liquid scintillation counting
Bibliographic record
Abstract
When responding to an emergency radiological incident, rapid methods are needed to provide the physicians and radiation protection personnel with an early estimation of possible internal dose resulting from the inhalation of radionuclides. This information is needed so that appropriate medical treatment and radiological protection control procedures can be implemented. Nasal swab analysis, which employs swabs swiped inside a nostril followed by liquid scintillation counting of alpha and beta activity on the swab, could provide valuable information to quickly identify contamination of the affected population. In this study, various parameters (such as alpha/beta discrimination, swab materials, counting time and volume of scintillation cocktail etc) were evaluated in order to optimise the effectiveness of the nasal swab analysis method. An improved nasal swab procedure was developed by replacing cotton swabs with polyurethane-tipped swabs. Liquid scintillation counting was performed using a Hidex 300SL counter with alpha/beta pulse shape discrimination capability. Results show that the new method is more reliable than existing methods using cotton swabs and effectively meets the analysis requirements for screening personnel in an emergency situation. This swab analysis procedure is also applicable to wipe tests of surface contamination to minimise the source self-absorption effect on liquid scintillation counting.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".