History of controlled trials in medicine: real priorities are little-known. Report 2. From early experiments to the present day: without alternation and randomization
Bibliographic record
Abstract
The aim of the three-report review is the historical development of clinical trials, controlled trials (CT) and randomized controlled trials (RCT), and the inclusion of these approaches in health-related disciplines (Medicine and Epidemiology). Report 2 provides a description of the wellknown James Lind Library (JLL), as well as a formed database of sources on the theme. JLL was internationalized, although most of the papers belong to authors from the UK. Many studies on the history of CT and RCT are reflected in JLL publications, but remain unclaimed without changing on common milestones and priorities. Besides, the formed base of sources included 9 studies not reflected in the JLL, of which three are principled. Six of them are given in Report 2. Half of historical milestones on the theme (168 in total) belong to the United Kingdom, 23% to the United States, and 4% to the Italy. The remaining 19 countries, ancient, medieval and modern, contribute 0.6–4% (Russia – 1.2% by the 20th century). The earliest source on the history of CT is J.P. Bull’s dissertation (1951). The formed database as of July 2020 contained more than 260 publications, and only 9 of them were Russian (2005–2018). The base includes 7 western dissertations on the history of CT. The object of the Report 2 study was CT as such, without any attempts at randomization or even quasi-randomization by alternate allocation. The most comprehensive thematic table on non-randomized CTs has been compiled, including studies from the Chinese emperor Shen Nung (2373 BC) and the prophet Daniel (6th century BC), to BCG vaccination for children of Canadian Indians (1941–1949). PubMed search on ‘non-randomized controlled trial’ was made. For the period of 1990–2020 years, 303 publications were found (up to 32 papers in 2020). Compared to RCT, the number of such studies is small (estimated at 0.08%), but it is important to have an appropriate conjuncture in the modern period. Along with the fact that most of the drugs and therapies currently in use are developed without RCT, the revealed ‘immortality’ of CT, even without quasi-randomization, can have social significance, removing complexes and embarrassment in cases where neither RCT nor even quasi-RCT is possible, but social and public needs require the immediate receipt of at least an approximate answer to hot questions of public health (for example, in 2020).
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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 | 0.202 | 0.446 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".