Tylectomy Safety in Salvage of Eyes with Retinoblastoma
Bibliographic record
Abstract
Intraocular surgery is tabooed in retinoblastoma management, due to the concern of lethal extraocular spread. We reviewed the outcomes of consecutive children with intraocular retinoblastoma diagnosed at 29 Chinese centers between 2012–2014. We compared the outcomes of three categories of treatment: eye salvage including tylectomy (Group I), eye salvage without tylectomy (Group II), and primary enucleation (Group III). A total of 960 patients (1243 eyes) were diagnosed: 256 in Group I, 370 in Group II, and 293 in Group III; 41 patients abandoned treatment upfront. The estimated 5-year overall survivals (OS) were, for Group I, 94%, for Group II 89%, and for Group III 95%. The estimated 5-year disease-specific survivals (DSS) were, for Group I, 96%, for Group II 90%, and for Group III 95%. Patients in Group I had a significantly higher 5-year DSS than patients in Group II (p = 0.003) and not significantly different than patients in Group III (p = 0.367). Overall survival was not compromised by the inclusion of tylectomy in eye salvage therapy compared to eye salvage without tylectomy or primary enucleation. Disease-specific survival was better when tylectomy was included in eye salvage treatments. Tylectomy as part of multimodal treatment may contribute to the care of retinoblastoma patients with chemotherapy-resistant tumor, eyes with concomitant ocular complications, or at the risk of treatment abandonment.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".