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Record W4280541608 · doi:10.3168/jds.2022-21782

Publication and accessibility of results of controlled trials in dairy science

2022· article· en· W4280541608 on OpenAlexafffund
Jan M. Sargeant, Annette M. O’Connor, Ellen R. Vriezen, Sarah C. Totton, S.J. LeBlanc

Bibliographic record

VenueJournal of Dairy Science · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of Guelph
FundersCanada First Research Excellence FundUniversity of Guelph
KeywordsSample size determinationClinical trialImpact factorMedicineInferenceSystematic reviewMEDLINEFamily medicinePsychologyComputer scienceInternal medicinePolitical scienceStatisticsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Research allows for the discovery of new knowledge and is integral to evidence-based decision-making. However, research is only useful if it is available. The aim of this study was to explore publication and accessibility of full-text reports for controlled trials (experimental studies) conducted in dairy cattle. We determined the proportion of controlled trials presented as abstracts at the 2015 Joint Annual Meeting of the American Dairy Science Association and the American Society of Animal Science or the 2015 American Association of Bovine Practitioners Annual Conference that were subsequently published. Factors associated with publication or non-publication in a peer-reviewed journal were evaluated using risk ratios. For trials that were subsequently published, we compared the sample size, numerical results, and inference between the conference abstract and the subsequent publication. Approximately half of the trials (177 out of 380) reported at conferences were subsequently published. Source conference, whether the conference abstract results were described as preliminary, whether there was at least one positive outcome, author affiliation, whether the trial involved deliberate disease induction, and total sample size were not strongly associated with subsequent publication. For trials that were published, the sample size differed between the conference proceedings and full publications for 22%, the numerical results differed in 29%, and the inference differed for 11%. We also evaluated whether trials included in 9 recent systematic reviews were in English and were available without subscription or cost. Of the 390 trials included in recent systematic reviews, approximately 40% were available only through subscription or access fee. These results suggest that publication and accessibility of research results is suboptimal, representing an area of wastage in dairy cattle research. Researchers should ensure that they publish the results of trials comprehensively in searchable publications, even if the results are not novel or do not detect expected differences, and, when possible, make the results available freely.

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)0.692
metaresearch head score (Gemma)0.925
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.308
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6920.925
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0080.012
Bibliometrics0.0130.022
Science and technology studies0.0020.009
Scholarly communication0.0150.018
Open science0.0050.008
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0120.002

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 head0.642
GPT teacher head0.546
Teacher spread0.096 · 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 designObservational
DomainReporting
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
Published2022
Admission routes2
Has abstractyes

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