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Evaluation of the clinical efficacy of marbofloxacin (Zeniquin) tablets for the treatment of canine pyoderma: an open clinical trial

2001· article· en· W2055927005 on OpenAlexaff
Manon Paradis, Louis M. Abbey, Brenda F. Baker, Michael J. Coyne, M. Hannigan, Daniel Joffe, Bernhard P. Pukay, A. Trettien, Stephen Waisglass, Jocelyn R. Wellington

Bibliographic record

VenueVeterinary Dermatology · 2001
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsAlberta Hospital EdmontonUniversité de MontréalCegep de Saint Hyacinthe
FundersPfizer
KeywordsPyodermaMedicineAdverse effectAnorexiaStaphylococcus intermediusVomitingDermatologySurgeryInternal medicineStaphylococcusStaphylococcus aureusBiology

Abstract

fetched live from OpenAlex

The efficacy and field safety of marbofloxacin (Zeniquin) for the treatment of superficial and deep bacterial pyoderma were evaluated. Seventy-two dogs were treated with 2.75 mg kg-1 of marbofloxacin orally once daily for 21 or 28 days. Sixty-two dogs (86%) had superficial pyoderma and 10 (14%) had deep pyoderma. A history of prior pyoderma was reported in 39/72 dogs. Pretreatment aerobic bacteriologic cultures of skin lesions were performed in 47 cases and the predominant pathogen isolated was Staphylococcus intermedius. Treatment was successful in 62/72 (86.1%) dogs, improvement was noted in 6/72 (8.3%) dogs and treatment failed in 4/72 (5.6%) dogs. Adverse effects associated with treatment included listlessness, anorexia, vomiting, soft stool, flatulence and polydipsia; these adverse effects were seen in only 6/81 dogs. Marbofloxacin was safe and effective for the treatment of superficial and deep pyoderma in dogs at the dosage used in this study.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.330
GPT teacher head0.516
Teacher spread0.186 · 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 designNon-randomized trial
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

Citations33
Published2001
Admission routes1
Has abstractyes

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