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The treatment of bilaterally advanced retinoblastoma: The Institut Curie experience

2013· article· en· W1982235068 on OpenAlexaff
Laurence Desjardins, L. LumbrosoLeRouic, Christine Lévy‐Gabriel, Nathalie Cassoux, Isabelle Aerts, Xavier Sastre, Alexia Savignoni

Bibliographic record

VenueActa Ophthalmologica · 2013
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsPediatric Oncology Group
Fundersnot available
KeywordsCryotherapyMedicineRetinoblastomaBrachytherapyChemotherapyVisual acuitySurgeryOphthalmologyRadiation therapy

Abstract

fetched live from OpenAlex

Abstract Purpose Since 2005 we have used more intensive chemotherapy, combined with intensive local treatment started at the third cycle in case of bilateral group D or group D+E bilateral retinoblastoma. We report the results Methods All children identified with bilaterally advanced retinoblastoma were treated with 6 courses of 3 drugs. Local treatments including laser, cryotherapy and sometimes iodine plaque brachytherapy were started at the third cycle, synergistic with the chemotherapy. All tumors except the macular tumors were treated with laser during 5 to 20 minutes. The inferior periphery was treated with cryotherapy . After the end of the chemotherapy, close follow up was performed and additional local treatments were often necessary. Data concerning the initial findings, treatments and results were entered in the data base. Results : the follow up ranges from 3 to 8 years with a mean follow up of five years. Between 2005 and 2010 23 group D eyes were treated in 16 children.17 eyes are preserved without external beam (73%) One eye is lost to follow up. Visual acuity is available in 11 children ranging from counting fingers to 20/20 with a mean visual acuity of 20/50. Conclusion Intravenous chemotherapy associated with intensive local treatments allows preservation of the eye with usefull vision in a great pourcentage of group D eyes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.135
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.309
Teacher spread0.284 · 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 teacher head, 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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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