PRIASE 2021 guidelines for reporting animal studies in Endodontology: explanation and elaboration
Bibliographic record
Abstract
Laws and ethics require that before conducting human clinical trials, a new material, device or drug may have to undergo testing in animals in order to minimize health risks to humans, unless suitable supporting grandfather data already exist. The Preferred Reporting Items for Animal Studies in Endodontology (PRIASE) 2021 guidelines were developed exclusively for the specialty of Endodontology by integrating and adapting the ARRIVE (Animals in Research: Reporting In Vivo Experiments) guidelines and the Clinical and Laboratory Images in Publications (CLIP) principles using a validated consensus-based methodology. Implementation of the PRIASE 2021 guidelines will reduce potential sources of bias and thus improve the quality, accuracy, reproducibility, completeness and transparency of reports describing animal studies in Endodontology. The PRIASE 2021 guidelines consist of a checklist with 11 domains and 43 individual items and a flowchart. The aim of the current document is to provide an explanation for each item in the PRIASE 2021 checklist and flowchart and is supplemented with examples from the literature in order for readers to understand their significance and to provide usage guidance. A link to the PRIASE 2021 explanation and elaboration document and PRIASE 2021 checklist and flowchart is available on the Preferred Reporting Items for study Designs in Endodontology (PRIDE) website (http://pride-endodonticguidelines.org/priase/).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.150 | 0.283 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.104 | 0.081 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".