Animal testing: a re‐evaluation of what it means to Endodontology
Bibliographic record
Abstract
Animal testing has a long history in the biological evaluation of medical devices used in Dentistry (ISO 7405). Indeed, tests on animals have been used to develop new interventions, materials, devices and drugs as they tend to provide information on their biological mechanisms of action, as well as their efficacy and side-effects before human applications (Singh et al. 2016). As an example, animal studies supported the basis for the understanding of orofacial development (Sharpe 1995). However, animal tests are not essential and may be prohibited by international and local laws, for example, the European Union animal testing ban for cosmetics (EU Regulation No 1223/2009) and the California Cruelty-Free Cosmetics Act (SB-1249). The animal testing bans indicate a public policy shift away from animal testing, to placing more reliance on the results from lab-on chip, organ-on-chip, human cell-based studies and clinical trials, to reduce the number of animals that are sacrificed and protect patient or consumer health and safety (Kimura et al. 2018). It has been estimated that 115 million animals are used for research testing worldwide each year, mostly in the United States, Japan, China, Australia, France, Canada, the United Kingdom, Germany and Brazil (Taylor et al. 2008). Animal testing is not in decline, and in many parts of the world it is on the increase or remains at the same level as in the 1980s or 1990s (Taylor et al. 2008). The most common animals used in testing are mice, rats, birds, sheep, guinea-pigs, monkeys, dogs, cats, pigs, goats and cows. Up to 97% of dog and cat owners regard their animals as a member of their family (Risely-Curtiss et al. 2006). Moving forward, it cannot be acceptable to publish tests done on dogs and cats, because they are most often viewed as family members, not laboratory animals. Animal testing can be made more morally and ethically acceptable, by trusting animal welfare to the extensive regulations governing animal tests. However, more protections to prevent animal pain and suffering are mandatory, in addition to the safety net of animal welfare regulations. Today, there can be no ethical justification to publish articles where there was a lack of pain monitoring, and where the pain relief measures appeared to be inadequate, to prevent avoidable animal suffering. Poor quality animal studies tend to produce clinically unreliable results (Pound & Bracken 2014, Singh et al. 2016), which can defeat the purpose of animal testing, rendering it useless. Steps to improve the quality of animal research, the ARRIVE (Animal Research: Reporting In Vivo Experiments) guidelines (Kilkenny et al. 2010) and the SYRCLE (Systematic Review Centre for Laboratory animal Experimentation) risk of bias tool (Hooijmans et al. 2014) were developed to guide researchers. However, animal studies in Endodontology often need exclusive information related to the specialty. Hence, we are proposing new guidelines named: ‘Preferred Reporting Items for Animal Studies in Endodontology (PRIASE)’, which can improve the quality of animal welfare and results, thereby improving the effectiveness, reproducibility and clinical translation of animal studies in Endodontology. With so much cognitive dissonance (inconsistent thoughts, beliefs and attitudes) towards animal testing, by wanting to keep benefiting from its safeguards, and to stop it at the same time, arriving at a consensus policy that satisfies all stakeholders may be impossible until we look at ourselves, our traditional values and our strengths. The field of Endodontology has attained an elevated social and professional purpose, due to our unique problem-solving skills to relieve and prevent pain, act with integrity and compassion, save teeth to improve a person's quality of life, give people the ability to eat and communicate, preserve a person's appearance and emotional identity. Now is the time to apply some of our unique skills and strengths to animal testing by developing and implementing the PRIASE guidelines.
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How this classification was reachedexpand
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".