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Record W7048462296

La sindrome di Horner nel cane: casi clinici (2009-2016) e revisione della letteratura.

2017· article· it· W7048462296 on OpenAlexaboutno aff

Bibliographic record

VenueElectronic Theses and Dissertations Repository (University of Pisa) · 2017
Typearticle
Languageit
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSigns and symptomsElectrodiagnosisCongenital disease
DOInot available

Abstract

fetched live from OpenAlex

Lo scopo di questa tesi è stato quello di analizzare la casistica clinica di un gruppo di cani affetti dalla sindrome di Horner esaminati presso l'Ospedale didattico Mario Modenato dell'Università di Pisa in un periodo di 82 mesi. Lo studio retrospettivo ha previsto tre criteri d'inclusione: la specie, la patologia diagnosticata in sede di visita oftalmica e clinica e la disponibilità di un follow up clinico o telefonico. Sono stati scelti 29 casi con diagnosi clinica di sindrome di Horner. Nel 51,72% dei soggetti è stato possibile formulare una diagnosi eziologica certa e nel 48,28% una diagnosi eziologica presuntiva. L'età media dei soggetti era 8,62 anni (range 1-15 anni) e le razze più colpite erano il Golden Retriever (41,38%) e Labrador Retriever (13,79%). La causa più frequente di sindrome di Horner è stata quella idiopatica nel 41,38% degli animali affetti. I risultati di questa tesi dimostrano che non è semplice formulare una diagnosi eziologica certa della sindrome di Horner, come peraltro già riportato in letteratura.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.224
Teacher spread0.216 · 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 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
Published2017
Admission routes1
Has abstractyes

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