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Record W4241595931 · doi:10.1002/9781119501237.ch124

Prostatic Diseases

2020· other· en· W4241595931 on OpenAlexaff
Serge Chalhoub

Bibliographic record

VenueClinical Small Animal Internal Medicine · 2020
Typeother
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProstatitisProstatic DiseasesChronic bacterial prostatitisMedicineEtiologyHyperplasiaProstateUrologyInternal medicinePathologyCancer

Abstract

fetched live from OpenAlex

Prostatic diseases are relatively common in middle-aged to older, intact male dogs and rare in neutered males, with a higher reported frequency in medium- and large-breed dogs. This chapter discusses the most common prostatic diseases, namely benign prostatic hyperplasia (BPH), bacterial acute and chronic prostatitis, prostatic abscesses, prostatic and paraprostatic cysts, and prostatic neoplasia. It also discusses the etiology, history and clinical signs, diagnosis, therapy, and prognosis of the disorders. In breeding dogs, medical therapy for BPH can be used to reduce prostatic size and clinical signs of prostatomegaly. Estrogen therapy can also be used to treat BPH and will cause a decrease in testosterone production. The most common bacterial organism to cause prostatitis is Escherichia coli. Other common infectious etiologies include Staphylococcus spp., Streptococcus spp., Mycoplasma spp., Pseudomonas spp., and Proteus spp.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0280.002

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 head0.200
GPT teacher head0.488
Teacher spread0.288 · 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
Published2020
Admission routes1
Has abstractyes

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