Concise Review: Increasing the Validity of Cerebrovascular Disease Models and Experimental Methods for Translational Stem Cell Research
Bibliographic record
Abstract
Interspecies differences, anatomical and physiological aspects, as wells as simplified study designs contribute to an overestimation of treatment effects and limit the transferability of experimental results into clinical applications. Confounders of cell therapies for cerebrovascular disorders (CVD) include common CVD comorbidities, frequent medications potentially affecting endogenous and transplanted stem cells, as well as age- and immune-system-related effects. All those can contribute to a substantial modeling bias, ultimately limiting the prospective quality of preclinical research programs regarding the clinical value of a particular cell therapy. In this review, we discuss the nature and impact of most relevant confounders. We provide suggestions on how they can be considered to enhance the validity of CVD models in stem cell research. Acknowledging substantial and sometimes surprising effects of housing conditions, chronobiology, and intersex differences will further augment the translational value of animal models. We finally discuss options for the implementation of high-quality functional and imaging readout protocols. Altogether, this might help to gain a more holistic picture about the therapeutic impact of a particular cell therapy for CVD, but also on potential side and off-site effects of the intervention. Stem Cells 2017;35:1141-1153.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".