Primary transpupillary thermotherapy for small suspicious choroidal nevi
Bibliographic record
Abstract
Abstract Purpose The purpose of this study was to assess the ocular and systemic outcomes of patients treated by transpupillary thermotherapy (TTT) for suspicious choroidal nevi at our oncology centre. Methods A retrospective chart review was conducted for all patients with a newly diagnosed small suspicious choroidal nevus treated by TTT at our oncology centre from the date of acquisition of the laser (2002) to September 2011. Standard treatment consisted of three TTT sessions. Patients with two or more risk factors for tumour growth were systematically treated. Ocular and systemic outcomes were reviewed and compared with those of similar patients that were observed between 1990 and 2008. Results Our preliminary data include 8 patients treated by TTT and 56 patients that were followed‐up without treatment. Of the treated patients, 3 (37.5%) showed progression with a mean time to recurrence of 13 months. Of the observed patients, 23 (41%) showed progression with a mean time to recurrence of 41 months. There were no reported deaths in the treated group, and 3 (5.4%) deaths due to metastatic melanoma in the observation group. Conclusion Despite careful patient selection, primary TTT for small suspicious choroidal nevi showed poor local tumour control. No deaths due to metastatic melanoma were reported in the follow‐up of treated patients, and mortality remained low in the observation group.
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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) | 0.000 | 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 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".