“Indocyanine green enhanced TTT Vs TTT for treatment of thicker tumors in Retinoblastoma- a randomised control trial”
Bibliographic record
Abstract
Background Transpupillary thermotherapy (TTT) is used as a focal treatment modality for smaller retinoblastoma tumors, however its efficacy in thicker tumors is limited. This study evaluated whether indocyanine green (ICG) augmentation of TTT (ICGeTTT) improves tumor regression compared to TTT alone for residual chemoreduced retinoblastomas >2 mm in height. Methods In this randomized controlled trial, simple randomisation table was used to allocate 28 chemoreduced tumors to treatment with either TTT (group 1) or ICGeTTT (group 2). Treatment was administered every 3–4 weeks until tumor height was <2 mm or a maximum of four sessions were completed. Results The baseline tumor height was higher for the ICGeTTT group. Adjusted analysis for this baseline difference showed a statistically significant greater reduction in tumor height for group 2 (44 %) as compared to group 1 (21 %) ( p = 0.018). The percentage of tumors achieving complete regression was higher in group 2 but this difference did not reach statistical significance. The cumulative energy used and side effects from treatment were similar between the groups. Conclusion Despite the limitations of the study including the small sample size and the baseline difference, the study still demonstrated a greater tumor height reduction with the use of ICGeTTT as compared to TTT in chemoreduced thick residual tumors and hence ICGeTTT may be preferred in such cases.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".