Invited review: Completeness of reporting of experiments: REFLECTing on a year of animal trials in the Journal of Dairy Science
Bibliographic record
Abstract
Reproducibility is an essential element of the scientific process, and it requires clear and complete reporting of study design, conduct, and analysis. In the human and animal health literature, incomplete reporting is associated with biased effect estimates. Moreover, incomplete reporting precludes knowledge synthesis and undervalues the resources allocated to the primary research. The Reporting Guidelines for Randomized Controlled Trials for Livestock and Food Safety (REFLECT) statement, published in 2010, is a checklist developed by expert consensus to provide guidance on what study elements should be reported in any intervention trial (designed experiment) involving livestock. The Journal of Dairy Science (JDS) has recently endorsed the use of reporting guidelines. To assess the status of reporting of controlled experiments in JDS and to provide a baseline for future comparison, we evaluated the reporting of 18 items from the REFLECT statement checklist in a sample of 137 controlled trials published in JDS in 2017. Two reviewers independently screened titles and abstracts for relevance and then evaluated a sample of 120 papers reporting controlled trials (experimental studies involving at least one intervention and one comparison or control group), using yes or no questions. Although some items, such as treatment details and statistical analysis, were well reported, other areas, including sample size justification, allocation concealment, blinding, study flow, baseline data, and ancillary analyses, were often not reported or were incompletely described. This work highlights the need for authors and reviewers to take advantage of guidelines and checklists for reporting. Adherence to reporting guidelines can help improve the completeness of reporting of research, expedite and better inform the peer-review process, increase clarity for the reader, and allow for knowledge synthesis, such as meta-analysis, all of which serve to increase the value of the work conducted.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Metaresearch Domain: Reporting · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.718 | 0.445 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.005 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.010 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".