Gamma Ray Nuclear Resonance Absorption: An Alternative Method for <i>in Vivo</i> Body Composition Studies
Bibliographic record
Abstract
We have evaluated gamma ray nuclear resonance absorption (gamma-NRA) on nitrogen, a mature technology proposed and developed by Soreq NRC for detecting explosives, as an alternative to neutron activation for in vivo assaying of body nitrogen. The principles of the gamma-NRA method are outlined, and a test facility constructed at McMaster University's Accelerator Laboratory is described. The results of a feasibility study recently performed there on phantoms and animal tissue are presented and discussed. gamma-NRA is a full imaging technique that essentially constitutes element-specific absorptiometry--i.e., it can generate projections of the mass distribution for a specific element, along with a conventional radiograph of the patient. From the transmission profile of an individual scanned by 9.17 MeV gamma rays, local or whole body nitrogen content can be determined via the resonant attenuation undergone when the beam encounters regions of nitrogen concentration. The advantages of gamma-NRA over neutron activation are (a) radiation doses delivered to the body are at least one order of magnitude lower, thus allowing repeated measurements on individual patients and also rendering the method ethically acceptable for application to children; (b) gamma-NRA is inherently free from uncertainties related to nonuniform distributions of the element in question within the body; (c) it is applicable to patients of varying size and shape; and (d) it yields both nitrogen images and conventional radiographic images of the body.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".