Surrogate Humane Endpoints in Small Animal Models of Acute Lung Injury: A Modified Delphi Consensus Study of Researchers and Laboratory Animal Veterinarians*
Bibliographic record
Abstract
OBJECTIVES: In many jurisdictions, ethical concerns require surrogate humane endpoints to replace death in small animal models of acute lung injury. Heterogenous selection and reporting of surrogate endpoints render interpretation and generalizability of findings between studies difficult. We aimed to establish expert-guided consensus among preclinical scientists and laboratory animal veterinarians on selection and reporting of surrogate endpoints, monitoring of these models, and the use of analgesia. DESIGN: A three-round consensus process, using modified Delphi methodology, with researchers who use small animal models of acute lung injury and laboratory animal veterinarians who provide care for these animals. Statements on the selection and reporting of surrogate endpoints, monitoring, and analgesia were generated through a systematic search of MEDLINE and Embase. Participants were asked to suggest any additional potential statements for evaluation. SETTING: A web-based survey of participants representing the two stakeholder groups (researchers, laboratory animal veterinarians). Statements were rated on level of evidence and strength of support by participants. A final face-to-face meeting was then held to discuss results. SUBJECTS: None. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Forty-two statements were evaluated, and 29 were rated as important, with varying strength of evidence. The majority of evidence was based on rodent models of acute lung injury. Endpoints with strong support and evidence included temperature changes and body weight loss. Behavioral signs and respiratory distress also received support but were associated with lower levels of evidence. Participants strongly agreed that analgesia affects outcomes in these models and that none may be necessary following nonsurgical induction of acute lung injury. Finally, participants strongly supported transparent reporting of surrogate endpoints. A prototype composite score was also developed based on participant feedback. CONCLUSIONS: We provide a preliminary framework that researchers and animal welfare committees may adapt for their needs. We have identified knowledge gaps that future research should address.
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 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 | 0.383 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".