‘Allocation concealment’: the evolution and adoption of a methodological term
Bibliographic record
Abstract
Random assignment of individual participants in clinical trials entails two steps: (i) generating an unbiased treatment allocation schedule; and (ii) applying the schedule without foreknowledge of upcoming allocations. These two steps were implicit in the famous randomized trial of streptomycin for pulmonary tuberculosis in 1948, and were recognized explicitly in some early books on controlled trials. However, half a century later, no widely accepted term denoting the process of concealing upcoming allocations had been adopted. In 1983 Thomas Chalmers and colleagues termed that process “randomization blinding,” and showed that blinded randomization and unblinded randomization were associated with differing estimates of treatment effects; however, their terminology was subsequently rarely used. In the mid-1990s we suggested that the term “allocation concealment” would be preferable to “blinded randomization,” particularly to avoid terminology that might be confused with blinding of treatments after random allocation. After controlling for more factors than had been accounted for by Chalmers and colleagues, we demonstrated an association between allocation concealment and estimates of treatment effects. Moreover, as further indication of bias, inadequately concealed trials displayed more heterogeneity than adequately concealed trials. Notably, our modeling and methodological approach to examine the associations between trial quality and estimates of treatment effects has gained recognition and achieved replication. A PubMed search for the term “allocation concealment” between 1972 and 1993 in “any field” yielded no instances, compared with 1471 between 1995 and 2016. Google Scholar found 25 matches before 1994 and over 30,000 matches after. Although the term might still be improved to avoid occasional misconceptions about its meaning, we assume that it has been widely adopted by authors and editors because they find the term useful.
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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 | 0.242 | 0.374 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.003 | 0.054 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.013 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".