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Record W4361276632 · doi:10.3389/fvets.2023.1137774

The standards of reporting randomized trials in pets (PetSORT): Methods and development processes

2023· article· en· W4361276632 on OpenAlexaff
Audrey Ruple, Jan M. Sargeant, Laura E. Selmic, Annette M. O’Connor

Bibliographic record

VenueFrontiers in Veterinary Science · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsConsolidated Standards of Reporting TrialsChecklistGuidelineClinical trialProtocol (science)Randomized controlled trialFamily medicineMedicineSystematic reviewGovernment (linguistics)MEDLINEAlternative medicinePsychologyMedical educationPathologyPolitical science

Abstract

fetched live from OpenAlex

Background: Reporting of clinical trials conducted in client- and shelter-owned dog and cat populations is not optimal, which inhibits the ability to assess the reliability and validity of trial findings and precludes the ability to include some trials in evidence synthesis. Objective: To develop a reporting guideline for parallel group and crossover trials that addresses the unique features and reporting requirements for trials conducted in client- and shelter-owned dog and cat populations. Design: Consensus statement. Setting: Virtual. Participants: Fifty-six experts from North America, the United Kingdom, Europe, and Australia working in academia, government (research and regulatory agencies), industry, and clinical veterinary practice. Methods: A steering committee created a draft checklist for reporting criteria based upon the CONSORT statement and the CONSORT extensions for reporting of abstracts and crossover trials. Each item was presented to the expert participants and was modified and presented again until >85% of participants were in agreement about the inclusion and wording of each item in the checklist. Results: . Conclusion: The methods and processes used to develop this guideline represent a novel departure from those used to create other reporting guidelines, by using a virtual format. The use of the PetSORT statement should improve reporting of trials conducted in client- and shelter-owned dogs and cats and published in the veterinary research literature.

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)0.028
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.104
GPT teacher head0.489
Teacher spread0.385 · 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 designBench or experimental
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

Citations10
Published2023
Admission routes1
Has abstractyes

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