Elemental analysis in living human subjects using biomedical devices
Bibliographic record
Abstract
Today, patients undergoing dialysis are at low risk for aluminum-induced dementia. Workers are unlikely to experience cadmium-induced emphysema and the public's exposure to lead is an order of magnitude lower than in 1970. The research field of in vivo elemental analysis has played a role in these occupational and environmental health improvements by allowing the effects of people's chronic exposure to elements to be studied using non-invasive, painless, and relatively low-cost technology. From the early 1960s to the present day, researchers have developed radiation-based systems to measure the elemental content of organs at risk or storage organs. This reduces the need for (sometimes painful) biopsy and the risk of infection. Research and development has been undertaken on forty-nine in vivo measurement system designs. Twenty-nine different in vivo elemental analysis systems, measuring 22 different elements, have been successfully taken from design and testing through to human measurement. The majority of these systems employ either neutron activation analysis or x-ray fluorescence analysis as the basis of the measurement. In this review, we discuss eight of the successful systems, explaining the rationale behind their development, the methodology, the health data that has resulted from application of these tools, and provide our opinion on potential future technical developments of these systems. We close by discussing four technologies that may lead to new directions and advances in the whole field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.003 | 0.003 |
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".