Accurately characterized optical tissue phantoms: how, why and when?
Bibliographic record
Abstract
Optical tissue phantoms are very important tools for the development of biomedical imaging applications. Optical phantoms are often used as ground truth against which instruments results can be compared. It is therefore important that the optical properties of reference phantoms be measured in a manner that is traceable to the international system of units. SI traceability insures long term consistency of results and will therefore improve the effectiveness of diffuse optics research effort more effective by reducing unwanted variability in the data produced and shared by the community. The ultimate benefit of rigorous SI traceability is the reduction of variability in the data produced by novel diagnostic devices, which will in turn increase the statistical power of clinical trials aiming at validating their clinical usefulness. SI traceability, and therefore uncertainty analysis, is also relevant to traceability aspects mandated by FDA regulations. SI traceability is achieved through a thorough analysis of the measurement principle and its potential error sources. The uncertainty analysis should be ultimately validated by inter-laboratory comparison until a consensus is attained on the best practices for measuring the optical properties of tissue phantoms.
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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 | 0.031 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".