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Record W4224074572 · doi:10.1016/j.ophtha.2022.04.006

High-risk Pathologic Features Based on Presenting Findings in Advanced Intraocular Retinoblastoma

2022· article· en· W4224074572 on OpenAlexaff
Ankit Singh Tomar, Paul T. Finger, Brenda Gallie, Tero Kivelä, Ashwin Mallipatna, Chengyue Zhang, Junyang Zhao, Matthew W. Wilson, Rachel C. Brennan, Michala Burges, Jonathan Kim, Jesse L. Berry, Rima Jubran, Vikas Khetan, Suganeswari Ganesan, А.А. Yarovoy, V.А. Yarovaya, Е.S. Kotova, D.P. Volodin, Yacoub A. Yousef, Kalle Nummi, Tatiana L. Ushakova, Olga V. Yugay, V. G. Polyakov, Marco A. Ramírez‐Ortiz, Elizabeth Esparza-Aguiar, Guillermo Chantada, Paula Schaiquevich, Adriana Fandiño, Jason C. Yam, Winnie Lau, Carol P. Lam, Phillipa Sharwood, Sonia Moorthy, Quah Boon Long, Vera Adobea Essuman, Lorna Renner, Ekaterina A. Semenova, Jaume Catalá‐Mora, Genoveva Correa-Llano, Elisa Carreras

Bibliographic record

VenueOphthalmology · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsSickKids FoundationPrincess Margaret Cancer CentreHospital for Sick Children
FundersNational Cancer Institute
KeywordsMedicineRetinoblastomaOphthalmologyMEDLINEOptometry

Abstract

fetched live from OpenAlex

PURPOSE: To determine the value of clinical features for advanced intraocular retinoblastoma as defined by the eighth edition of the American Joint Committee on Cancer (AJCC) cT3 category and AJCC Ophthalmic Oncology Task Force (OOTF) Size Groups to predict the high-risk pathologic features. DESIGN: International, multicenter, registry-based retrospective case series. PARTICIPANTS: Eighteen ophthalmic oncology centers from 13 countries over 6 continents shared evaluations of 942 eyes enucleated as primary treatment for AJCC cT3 and, for comparison, cT2 retinoblastoma. METHODS: International, multicenter, registry-based data were pooled from patients enrolled between 2001 and 2013. High-risk pathologic features were defined as AJCC categories pT3 and pT4. In addition, AJCC OOTF Size Groups were defined as follows: (1) less than half, (2) more than half but less than two thirds, (3) more than two thirds of globe volume involved, and (4) diffuse infiltrating retinoblastoma. MAIN OUTCOME MEASURES: Statistical risk of high-risk pathologic features corresponding to AJCC cT3 subcategories and AJCC OOTF Size Groups. RESULTS: Of 942 retinoblastoma eyes treated by primary enucleation, 282 (30%) showed high-risk pathologic features. Both cT subcategories and AJCC OOTF Size Groups (P < 0.001 for both) were associated with high-risk pathologic features. On logistic regression analysis, cT3c (iris neovascularization with glaucoma), cT3d (intraocular hemorrhage), and cT3e (aseptic orbital cellulitis) were predictive factors for high-risk pathologic features when compared with cT2a with an odds ratio of 2.3 (P = 0.002), 2.5 (P = 0.002), and 3.3 (P = 0.019), respectively. Size Group 3 (more than two-thirds globe volume) and 4 (diffuse infiltrative retinoblastoma) were the best predictive factors with an odds ratio of 3.3 and 4.1 (P < 0.001 for both), respectively, for high-risk pathologic features when compared with Size Groups 1 (i.e., < 50% of globe volume). CONCLUSIONS: The AJCC retinoblastoma staging clinical cT3c-e subcategories (glaucoma, intraocular hemorrhage, and aseptic orbital cellulitis, respectively) as well as the AJCC OOTF Size Groups 3 (tumor more than two thirds of globe volume) and 4 (diffuse infiltrative retinoblastoma) both allowed stratification of clinical risk factors that can be used to predict the presence of high-risk pathologic features and thus facilitate treatment decisions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.010
GPT teacher head0.280
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations26
Published2022
Admission routes1
Has abstractno

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