The methodological quality of animal research in critical care: the public face of science
Bibliographic record
Abstract
BACKGROUND: Animal research (AR) findings often do not translate to humans; one potential reason is the poor methodological quality of AR. We aimed to determine this quality of AR reported in critical care journals. METHODS: All AR published from January to June 2012 in three high-impact critical care journals were reviewed. A case report form and instruction manual with clear definitions were created, based on published recommendations, including the ARRIVE guidelines. Data were analyzed with descriptive statistics. RESULTS: Seventy-seven AR publications were reviewed. Our primary outcome (animal strain, sex, and weight or age described) was reported in 52 (68%; 95% confidence interval, 56% to 77%). Of the 77 publications, 47 (61%) reported randomization; of these, 3 (6%) reported allocation concealment, and 1 (2%) the randomization procedure. Of the 77 publications, 31 (40%) reported some type of blinding; of these, disease induction (2, 7%), intervention (7, 23%), and/or subjective outcomes (17, 55%) were blinded. A sample size calculation was reported in 4/77 (5%). Animal numbers were missing in the Methods section in 16 (21%) publications; when stated, the median was 32 (range 6 to 320; interquartile range, 21 to 70). Extra animals used were mentioned in the Results section in 31 (40%) publications; this number was unclear in 23 (74%), and >100 for 12 (16%). When reporting most outcomes, numbers with denominators were given in 35 (45%), with no unaccounted numbers in 24 (31%), and no animals excluded from analysis in 20 (26%). Most (49, 64%) studies reported >40, and another 19 (25%) reported 21 to 40 statistical comparisons. Internal validity limitations were discussed in 7 (9%), and external validity (to humans) discussed in 71 (92%), most with no (30, 42%) or only a vague (9, 13%) limitation to this external validity mentioned. CONCLUSIONS: The reported methodological quality of AR was poor. Unless the quality of AR significantly improves, the practice may be in serious jeopardy of losing public support.
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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 | 0.720 | 0.866 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| 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".