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Record W2805804519 · doi:10.1080/17469899.2018.1478729

Precision laser therapy for retinoblastoma

2018· article· en· W2805804519 on OpenAlexaff
Sameh E. Soliman, Stephanie N. Kletke, Kelsey A. Roelofs, Cynthia VandenHoven, Leslie Mckeen, Brenda L. Gallie

Bibliographic record

VenueExpert Review of Ophthalmology · 2018
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsRetinoblastomaMedicineLaserLaser therapyOptical coherence tomographyOphthalmologyOptics

Abstract

fetched live from OpenAlex

Introduction: Laser therapy is a cornerstone for control of intraocular retinoblastoma, after chemotherapy has brought the disease under initial control. Since first described over 6 decades ago, laser technologies and approaches have evolved to improve tumor control. Despite its important role, few publications describe techniques, types of lasers, and modes of delivery for retinoblastoma.Areas covered: The physical and optical properties of lasers, mechanisms of action, delivery systems and complications are described. Hand-held optical coherence tomography (OCT) detects microscopic retinoblastoma tumors and guides treatment, achieving precision primary therapy and elimination of recurrences.Expert commentary: In all the excitement of new therapies to cure intraocular retinoblastoma, laser treatment always compliments but is rarely mentioned. Hand-held OCT now adds precision to put laser in the forefront in achieving cure of retinoblastoma.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.038
GPT teacher head0.410
Teacher spread0.372 · 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.

Study designNot applicable
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

Citations14
Published2018
Admission routes1
Has abstractyes

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