Calibration standards for multicenter clinical trials of fluorescence spectroscopy for in vivo diagnosis
Bibliographic record
Abstract
In the context of clinical trials, calibration protocols for optical instruments that ensure measurement accuracy and the ability to carry out meaningful comparisons of data acquired from multiple instruments are required. A series of calibration standards and procedures are presented to assess technical feasibility of optical devices for cervical precancer detection. Measurements of positive and negative standards, and tissue are made with two generations of research grade spectrometers. Calibration accuracy, ability of standards to correct and account for changes in experimental conditions, and device components are analyzed. The relative frequency of measured calibration standards is investigated retrospectively using statistical analysis of trends in instrument performance. Fluorescence measurements of standards and tissue made with completely different spectrometers show good agreement in intensity and lineshape. Frequency of wavelength calibration standards is increased to every 2 h to compensate for thermal drifts in grating mount. Variations in illumination energy detected between standards and patient measurements require probe redesign to allow for simultaneous acquisition of illumination power with every patient measurement. The use of frequent and well-characterized standards enables meaningful comparison of data from multiple devices and unambiguous interpretation of experiments among the biomedical optics community.
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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 | 0.371 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".