Optimal excitation wavelengths for discrimination of cervical neoplasia
Bibliographic record
Abstract
Fluorescence spectroscopy has shown promise for the in vivo, real-time detection of cervical neoplasia. However, selection of excitation wavelength has in the past been based on in vitro studies and the availability of light sources. The goal of this study was to determine optimal excitation wavelengths for in vivo detection of cervical neoplasia. Fluorescence excitation-emission matrices (EEMs) were measured in vivo from 351 sites in 146 patients. Data were analyzed in pairs of diagnostic classes to determine which combination of excitation wavelengths yields classification algorithms with the greatest sensitivity and specificity. We find that 330-340-, 350-380-, and 400-450-nm excitation yield the best performance. The sensitivity and specificity for discrimination of squamous normal tissue and high-grade squamous intraepithelial lesion (HGSIL) were 71% and 77% on cross validation using three excitation wavelengths. These results are comparable with those found in earlier in vivo studies; however, in this study we find that the proportion of samples which are HGSIL influences performance. Furthermore stratification of samples within low-grade squamous intraepithelial lesion and HGSIL also appears to influence diagnostic performance. Future diagnostic studies should be carried out at these excitation wavelengths in larger groups so that data can be stratified by diagnostic subcategory, age and menopausal status. Similarly, large studies should be done in screening populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".