Photodynamic Therapy in Barrett’s Esophagus: Results of Treatment of 17 Patients
Bibliographic record
Abstract
BACKGROUND: Barrett's esophagus (BE) with dysplasia may progress to esophageal adenocarcinoma. Photodynamic therapy is a promising treatment for BE. OBJECTIVE: To determine if photodynamic therapy is an acceptable alternative to esophagectomy in BE patients with high-grade dysplasia or early adenocarcinoma. METHODS: Seventeen patients were treated with photodynamic therapy for BE and high-grade dysplasia or early esophageal adenocarcinoma. Patients with residual Barrett's epithelium were treated with supplemental argon plasma coagulation or potassium titanyl phosphate laser. Patients underwent follow-up endoscopy three, six, nine and 12 months post-treatment, then every six to 12 months. Mean follow-up was 21 months. RESULTS: High-grade dysplasia or early adenocarcinoma was completely eliminated in nine of 15 (60%) patients. High-grade dysplasia was downgraded in one patient, persisted in one patient and progressed in four patients. Two patients with early esophageal adenocarcinoma were nonresponders. Complications included stricture, sunburn, urticaria, small pleural effusions, esophageal spasm and transient atrial fibrillation. CONCLUSIONS: Photodynamic therapy with supplemental ablation is a good, noninvasive therapy for elimination of high-grade dysplasia and early adenocarcinoma in BE. Failure to eliminate dysplastic epithelium occurred in 40% of the patients, thereby necessitating careful follow-up.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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 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".