Instrumentation as a source of variability in the application of fluorescence spectroscopic devices for detecting cervical neoplasia
Bibliographic record
Abstract
We report on a study designed to assess variability among three different fluorescence spectroscopy devices, four fiber optic probes, and three sets of optical calibration standards to better understand the reproducibility of measurements and interdevice comparisons of fluorescence spectroscopic data intended for clinical diagnostic use. Multiple measurements are acquired from all sets of standards using each combination of spectrometer, fiber optic probe, and optical standard. Data are processed using standard calibration methods to remove instrument-dependant responses. Processed spectra are analyzed using an analysis of variance to assess the percent variance explained by each factor that was statistically significant. Analysis of processed data confirms statistically significant differences among the spectrometers and fiber optic probes. However, no differences are found when varying calibration standards or measurement date and time. The spectrometers and fiber optic probes are significant sources of variability, but appropriate data processing substantially reduces these effects. Studies of inter- and intradevice variability are important methodological issues for optical device trials and must be included in the quality assurance studies for the clinical trial design.
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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 | 0.057 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".