An alternative pathway for preclinical research in fluid management
Bibliographic record
Abstract
Recent meta-analyses have created uncertainties regarding the appropriate clinical role of colloid resuscitation fluids in critically ill patients and prompted changes in fluid management practice. Such changes may not be justified in view of methodological limitations inherent in the meta-analyses. Further research is nevertheless needed to resolve the questions raised concerning the relationship between choice of resuscitation fluid and patient outcome. Animal studies can play an important part by reliably indicating whether particular fluids are likely to prove effective and safe in clinical trials. It is important to avoid costly large-scale clinical trials that fail to demonstrate the clinical utility of the tested therapy, as resources expended in failed trials raise overall development costs and thereby restrict the range of therapies meeting criteria of commercial feasibility. Promising therapies may thus not be pursued, even though an urgent clinical need may exist. An alternative pathway of preclinical research may be of value in avoiding some of the major clinical trial failures of recent years, particularly in the area of sepsis. This alternative pathway commences with the formulation of hypotheses by therapeutics developers. Independent preclinical investigators are challenged, by means of a competitive request for proposals, to test the hypotheses in rigorous randomized studies employing clinically relevant animal models. Promising proposals would then be selected for further development with the aid of peer review. The results of the randomized animal studies, along with other preclinical data, could also be evaluated using accepted principles of 'critical appraisal' commonly applied to clinical trial results. This critical appraisal might, where appropriate, include meta-analysis of animal study findings. This alternative preclinical pathway to new product evaluation should be completed before the commencement of large-scale clinical trials.
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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 | 0.472 | 0.538 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.012 | 0.013 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".