Non-invasive detection of osteoporotic bone loss using photothermal radiometry and modulated luminescence
Bibliographic record
Abstract
Osteoporosis is a skeletal disorder characterized by a compromised bone strength predisposing a person to an increased risk of fracture. The early detection of osteoporosis is important to a successful treatment. Current prominent bone densitometry techniques include, among others, Dual Energy X-Ray Absorptiometry (DEXA) and Mechanical Response Tissue Analysis (MRTA). However, DEXA uses ionizing radiation and MRTA results are often unreliable. Simultaneous Photothermal Radiometry (PTR) and Modulated Luminescence (LUM) measurements can be a non-ionizing, noninvasive and reliable alternative to the aforementioned diagnostics techniques. Controlled mineral loss was simulated with sequential etching of a human skull bone. During the experiments, a low-power modulated laser illuminated the sample surface. The absorbed incident optical energy was then re-emitted either non-radiatively, in the form of thermal waves (PTR), or radiatively as lumimescence light emission (LUM). The experimental setup consisted of a semiconductor laser (635 nm, 20 mW), two lock-in amplifiers, a mercury-cadmium-telluride IR detector for PTR, a photodiode for LUM and a computer. A one-dimensional, one-layer theoretical model for LUM and PTR was developed to analyze the experimental data and extract optical and thermal properties of the sample.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".