Detailed account of confounding factors in interpretation of FTIR spectra of exfoliated cervical cells
Bibliographic record
Abstract
The confounding variables that can potentially lead to a misinterpretation of FTIR spectroscopy of exfoliated cervical cells is described. A detailed account of the spectral effects of the following variables in FTIR spectroscopic screening of exfoliated cervical cells is presented: polymorphs; Cell degradation; and impurities such as endocervical columnar cells, metaplastic cells, cervical mucus, red cells, and debris. The interpretation of the spectra of exfoliated cervical cells must be done with subtraction analysis, which includes these factors. This is essential to prevent unacceptable false-positive rates. The above techniques are subsequently applied to two clinic populations: a dysplasia clinic in follow-up patients with negative cytology and two general gynecology clinics with patients with negative cytology. In the dysplasia clinic group 250 sequential patients with negative smears were tested. Thirty had false-positive smears as defined by the IR spectroscopy using the above methodology. Twenty of those patients subsequently had one follow-up and six had a positive abnormal smear. In the community clinic group 656 sequential patients were examined who had negative smears, of which 27 had false-positive FTIR spectra.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".