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Record W2905364739 · doi:10.1159/000494977

Tele-Oncology: A Validation Study of Choroidal and Iris Nevi

2018· article· en· W2905364739 on OpenAlexaff
Kelsey A. Roelofs, Ezekiel Weis

Bibliographic record

VenueOcular Oncology and Pathology · 2018
Typearticle
Languageen
FieldMedicine
TopicOcular Oncology and Treatments
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineInternal medicineClinical OncologyIRIS (biosensor)OncologyDermatologyChoroidPredictive valueOphthalmologyCancer

Abstract

fetched live from OpenAlex

<b><i>Background/Aims:</i></b> To evaluate teleophthalmological assessment of choroidal and iris nevi (tele-oncology) compared to traditional in-person clinical evaluation for detection of either axial or basal growth. <b><i>Methods:</i></b> This is a validation study. All 97 eyes of 99 patients were evaluated with an in-person ocular oncology visit utilizing standard testing, and subsequently had a tele-oncology evaluation with the standardized tests. The tele-oncology reviewer was blinded to the in-person examination findings. The primary study outcome was detection of nevus growth on tele-oncology compared to in-person clinical examination. <b><i>Results:</i></b> Patients had a mean age of 61 years and the majority had nevi located in the choroid (<i>n</i> = 87; 88%). The most common diagnosis was a low-risk nevus (<i>n</i> = 38; 44%). By tele-oncology assessment, 11 eyes showed growth. Ten of these patients had growth confirmed on in-person clinical examination. Resultantly, tele-oncology assessment of choroidal and iris nevi growth had a sensitivity of 100%, specificity of 99%, positive predictive value (PPV) of 91%, and negative predictive value (NPV) of 100%. <b><i>Conclusions:</i></b> The results of this study suggest that tele-oncology is a safe platform for monitoring choroidal and iris nevi for growth, with excellent sensitivity, specificity, PPV, and NPV.

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)0.001
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 score0.113
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.026
GPT teacher head0.349
Teacher spread0.323 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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