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Record W4253275200 · doi:10.1002/9781119108603.ch14

Acquired diseases of the orbit

2018· other· en· W4253275200 on OpenAlexaff
Robert L. Peiffer, Brian Wilcock

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsUniversity of GuelphUniversity of Saskatchewan
Fundersnot available
KeywordsOrbit (dynamics)Magnetic resonance imagingExophthalmosMedicineEyelidEnophthalmosRadiologyStrabismusOrbital DiseasesAnatomyDiplopiaSurgeryComputed tomography

Abstract

fetched live from OpenAlex

Orbital disease is much more prevalent in dogs and cats than in other species. Clinical signs may suggest the location and nature of the lesion; protrusion of the third eyelid and exotropia suggests involvement of the medial orbit. Orbital disorders encompass orbital inflammatory disease and hematomas, neoplasia, zygomatic sialocoele, vascular anomalies, and acquired orbital cysts and pseudocysts (mucoceles). This chapter focuses one these entities. Imaging modalities of ultrasound, computerized tomography (CT), and magnetic resonance imaging (MRI) are invaluable tools in the diagnosis of orbital disease. Both MRI and CT features correlate reasonably well with the histopathologic diagnoses of orbital disorders. Clinical evaluation of the orbit is facilitated by an appreciation of orbital anatomy. Enlargement of component structures by inflammation or neoplasia usually results in deviation of the globe and most commonly exophthalmos, occasionally enophthalmos and third eyelid prolapse, globe deviation from its normal axis, and/or protrusion of the third eyelid.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.004

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 head0.014
GPT teacher head0.275
Teacher spread0.262 · 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 designCase report
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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Citations0
Published2018
Admission routes1
Has abstractyes

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