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Methods and processes of developing the strengthening the reporting of observational studies in epidemiology − veterinary (STROBE-Vet) statement

2016· article· en· W2551162415 on OpenAlexaffabout
Jan M. Sargeant, Annette M. O’Connor, Ian R. Dohoo, Hollis N. Erb, Myriam Cevallos, Matthias Egger, Annette Kjær Ersbøll, Sylvia Martin, Liza Rosenbaum Nielsen, David L. Pearl, Dirk U. Pfeiffer, Javier Sánchez, Mary E. Torrence, Håkan Vigre, Cheryl Waldner, Michael P. Ward

Bibliographic record

VenuePreventive Veterinary Medicine · 2016
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsUniversity of Prince Edward IslandUniversity of SaskatchewanUniversity of Guelph
Fundersnot available
KeywordsObservational studyStrengthening the reporting of observational studies in epidemiologyAnimal welfareMedicineAnimal healthSystematic reviewStatement (logic)Family medicineWelfareMEDLINEVeterinary medicineMedical educationPsychologyEnvironmental healthPolitical sciencePathologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The reporting of observational studies in veterinary research presents many challenges that often are not adequately addressed in published reporting guidelines. OBJECTIVE: To develop an extension of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement that addresses unique reporting requirements for observational studies in veterinary medicine related to health, production, welfare, and food safety. DESIGN: A consensus meeting of experts was organized to develop an extension of the STROBE statement to address observational studies in veterinary medicine with respect to animal health, animal production, animal welfare, and food safety outcomes. SETTING: Consensus meeting May 11-13, 2014 in Mississauga, Ontario, Canada. PARTICIPANTS: Seventeen experts from North America, Europe, and Australia attended the meeting. The experts were epidemiologists and biostatisticians, many of whom hold or have held editorial positions with relevant journals. METHODS: Prior to the meeting, 19 experts completed a survey about whether they felt any of the 22 items of the STROBE statement should be modified and if items should be added to address unique issues related to observational studies in animal species with health, production, welfare, or food safety outcomes. At the meeting, the participants were provided with the survey responses and relevant literature concerning the reporting of veterinary observational studies. During the meeting, each STROBE item was discussed to determine whether or not re-wording was recommended, and whether additions were warranted. Anonymous voting was used to determine whether there was consensus for each item change or addition. RESULTS: The consensus was that six items needed no modifications or additions. Modifications or additions were made to the STROBE items numbered: 1 (title and abstract), 3 (objectives), 5 (setting), 6 (participants), 7 (variables), 8 (data sources/measurement), 9 (bias), 10 (study size), 12 (statistical methods), 13 (participants), 14 (descriptive data), 15 (outcome data), 16 (main results), 17 (other analyses), 19 (limitations), and 22 (funding). LIMITATION: Published literature was not always available to support modification to, or inclusion of, an item. CONCLUSION: The methods and processes used in the development of this statement were similar to those used for other extensions of the STROBE statement. The use of this extension to the STROBE statement should improve the reporting of observational studies in veterinary research related to animal health, production, welfare, or food safety outcomes by recognizing the unique features of observational studies involving food-producing and companion animals, products of animal origin, aquaculture, and wildlife.

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)0.773
metaresearch head score (Gemma)0.819
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.227
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.7730.819
Meta-epidemiology (narrow)0.0060.006
Meta-epidemiology (broad)0.0060.012
Bibliometrics0.0270.018
Science and technology studies0.0070.009
Scholarly communication0.0150.010
Open science0.0100.017
Research integrity0.0120.017
Insufficient payload (model declined to judge)0.0210.012

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 head0.859
GPT teacher head0.687
Teacher spread0.171 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations95
Published2016
Admission routes2
Has abstractyes

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