A Critical Appraisal of Experimental Intracerebral Hemorrhage Research
Bibliographic record
Abstract
The likelihood of translating therapeutic interventions for stroke rests on the quality of preclinical science. Given the limited success of putative treatments for ischemic stroke and the reasons put forth to explain it, we sought to determine whether such problems hamper progress for intracerebral hemorrhage (ICH). Approximately 10% to 20% of strokes result from an ICH, which results in considerable disability and high mortality. Several animal models reproduce ICH and its underlying pathophysiology, and these models have been widely used to evaluate treatments. As yet, however, none has successfully translated. In this review, we focus on rodent models of ICH, highlighting differences among them (e.g., pathophysiology), issues with experimental design and analysis, and choice of end points. A Pub Med search for experimental ICH (years: 2007 to 31 July 2011) found 121 papers. Of these, 84% tested neuroprotectants, 11% tested stem cell therapies, and 5% tested rehabilitation therapies. We reviewed these to examine study quality (e.g., use of blinding procedures) and choice of end points (e.g., behavioral testing). Not surprisingly, the problems that have plagued the ischemia field are also prevalent in ICH literature. Based on these data, several recommendations are put forth to facilitate progress in identifying effective treatments for ICH.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".