Are You "Tilting at Windmills" or Undertaking a Valid Clinical Trial?
Bibliographic record
Abstract
In this review, several aspects surrounding the choice of a therapeutic intervention and the conduct of clinical trials are discussed. Some of the background for why human studies have evolved to their current state is also included. Specifically, the following questions have been addressed: 1) What criteria should be used to determine whether a scientific discovery or invention is worthy of translation to human application? 2) What recent scientific advance warrants a deeper understanding of clinical trials by everyone? 3) What are the different types and phases of a clinical trial? 4) What characteristics of a human disorder should be noted, tracked, or stratified for a clinical trial and what inclusion /exclusion criteria are important to enrolling appropriate trial subjects? 5) What are the different study designs that can be used in a clinical trial program? 6) What confounding factors can alter the accurate interpretation of clinical trial outcomes? 7) What are the success rates of clinical trials and what can we learn from previous clinical trials? 8) What are the essential principles for the conduct of valid clinical trials?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".