The Internet and Clinical Trials: Background, Online Resources, Examples and Issues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Both the Internet and clinical trials were significant developments in the latter half of the twentieth century: the Internet revolutionized global communications and the randomized controlled trial provided a means to conduct an unbiased comparison of two or more treatments. Large multicenter trials are often burdened with an extensive development time and considerable expense, as well as significant challenges in obtaining, backing up and analyzing large amounts of data. Alongside the increasing complexities of the modern clinical trial has grown the power of the Internet to improve communications, centralize and secure data as well as to distribute information. As more and more clinical trials are required to coordinate multiple trial processes in real time, centers are turning to the Internet for the tools to manage the components of a clinical trial, either in whole or in part, to produce lower costs and faster results. This paper reviews the historical development of the Internet and the randomized controlled trial, describes the Internet resources available that can be used in a clinical trial, reviews some examples of online trials and describes the advantages and disadvantages of using the Internet to conduct a clinical trial. We also extract the characteristics of the 5 largest clinical trials conducted using the Internet to date, which together enrolled over 26000 patients.
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
| gpt | Metaresearch Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.333 | 0.745 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it