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Record W4211044916 · doi:10.1002/9781119108603.ch15

Intraocular neoplasia

2018· other· en· W4211044916 on OpenAlexaff
Robert L. Peiffer, Brian Wilcock

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of GuelphUniversity of Saskatchewan
Fundersnot available
KeywordsCATSCiliary bodyMalignancyPathologyMedicineNeoplasmEye neoplasmBiologyInternal medicine

Abstract

fetched live from OpenAlex

Neoplasms within the animal's globe may be primary (arising from one of the intraocular tissues), secondary (invading the globe from an adjacent tissue), or metastatic (arising via hematogenous dissemination of a distant malignancy). Uveal melanocytic neoplasia is the most common primary intraocular neoplasm, occurring roughly three times as frequently as ciliary body epithelial neoplasms in dogs and 10 times as frequently as ciliary body epithelial neoplasms in cats. Other primary intraocular neoplasms include posttraumatic sarcomas of cats and rarely other species such as the rabbit, schwannomas of blue eyed dogs, astrocytomas, and primary neuroectodermal neoplasms including medulloepitheliomas and retinoblastomas in varied animal species. Secondary neoplasia is most commonly encountered with squamous cell carcinoma of cow, horse, and cat, and nasal adenocarcinomas with orbital extension in dogs. With diagnosis in hand the clinician must consider management options for the particular animal affected.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.183
Threshold uncertainty score0.979

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)0.2050.022

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 head0.032
GPT teacher head0.368
Teacher spread0.336 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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