Translational Intracerebral Hemorrhage Research: Has Current Neuroprotection Research ARRIVEd at a Standard for Experimental Design and Reporting?
Bibliographic record
Abstract
One major aim of preclinical intracerebral hemorrhage (ICH) research is to develop and test potential neuroprotectants. Published guidelines for experimental design and reporting stress the importance of clearly and completely reporting results and methodological details to ensure reproducibility and maximize information availability. The current review has two objectives: first, to characterize current ICH neuroprotection research and, second, to analyze aspects of translational design in preclinical ICH studies. Translational design is the adoption and reporting of experimental design characteristics that are thought to be clinically relevant and critical to reproducibility in animal studies (e.g., conducting and reporting experiments according to the STAIR and ARRIVE guidelines, respectively). Given that ICH has no current neuroprotective treatments and an ongoing reproducibility crisis in preclinical research, translational design should be considered by investigators. We conducted a systematic review of ICH research from 2015 to 2019 using the PubMed database. Our search returned 281 published manuscripts studying putative neuroprotectants in animal models. Contemporary ICH research predominantly uses young, healthy male rodents. The collagenase model is the most commonly used. Reporting of group sizes, blinding, and randomization are almost unanimous, but group size calculations, mortality and exclusion criteria, and animal model characteristics are infrequently reported. Overall, current ICH neuroprotection research somewhat aligns with experimental design and reporting guidelines. However, there are areas for improvement. Because failure to consider translational design is associated with inflation of effect sizes (and possibly hindered reproducibility), we suggest that researchers, editors, and publishers collaboratively consider enhanced adherence to published guidelines.
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 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 | 0.027 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| 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".