Raman spectroscopy for optical diagnosis in normal and cancerous tissue of the nasopharynx—preliminary findings
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Raman spectroscopy (RS), which can detect molecular changes associated with cancer, was explored as a means of distinguishing normal and cancerous nasopharyngeal tissue. STUDY DESIGN/PATIENTS AND METHODS: Tissue from six patients with normal and cancerous biopsies was studied using a rapid acquisition Raman spectrometer. RESULTS: Spectra were obtainable within 5 seconds. Consistent differences were noted between normal and cancer tissue in three bands 1,290-1,320 cm(-1) (P = 0.005), 1,420-1,470 cm(-1) (P = 0.006), and 1,530-1,580 cm(-1) (P = 0.002). CONCLUSIONS: Spectral differences appear to exist between normal and cancerous nasopharyngeal tissue. The ability to obtain spectra rapidly supports the potential for future in vivo application.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".