<title>Fluorescence spectroscopy and imaging for detection of colonic dysplasia</title>
Bibliographic record
Abstract
Cancer of the colon is the second leading cause of cancer related death in North America and Europe. Colonoscopy is currently the gold standard for the detection and removal of polyps and the diagnosis of cancer. Although a field of intense research, there are currently no surrogate serum or stool markers that accurately identify patients at risk who may have adenomatous colon polyps or curable cancer. Clinicians therefore rely on white light colonoscopy to survey colonic mucosa in their search for poiyps. White light endoscopy has some limitations. It cannot detect flat, non raised lesions. It cannot distinguish easily between hyperplastic and adenomatous polyps. Subtle, flat lesions may be missed. Scars at the sites ofprevious sessile polyps are difficult to evaluate for recurrence. Endoscopies utilizing fluorescence techniques either on the basis of intrinsic fluorescence or exogenous (prodrug) compounds, have the potential to compliment white light endoscopy by improving detection of mucosal dysplasia and ultimately improve outcomes for cancer detection before the date of escape from cure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".