Design and rationale for an empirical investigation of the resource use and costs of investigator-initiated randomized trials in Switzerland, the UK, and Germany
Bibliographic record
Abstract
BACKGROUND: Conducting high-quality randomized clinical trials (RCTs) is challenging, time consuming, and resource intense. Academic investigators usually depend on scarce financial resources; however, current literature lacks systematically collected empirical data on the detailed resource use and costs of investigator-initiated RCTs. METHODS: The aim of this study is to generate a database of detailed empirical resource use and cost data from 100 investigator-initiated RCTs in Switzerland, Germany, and the UK. Investigators enter their empirical costs data into an online data collection form, which is followed by a short interview and a detailed cost report. We plan to investigate cost patterns and cost drivers and examine planned versus actual RCT costs as well as explore different strata of costs across the planning, conduct, and finalization phases, in drug and non-drug trials, and across medical fields and countries. DISCUSSION: This study will add detailed empirical data to the limited research on investigator-initiated RCT costs currently available. A study limitation will be that cost data will be retrospective and self-reported, which might be inaccurate depending on how costs were recorded. TRIAL REGISTRATION: Open Science Framework (OSF) https://doi.org/10.17605/OSF.IO/QY2GU . Registered on June 4, 2021.
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 | 0.408 | 0.630 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".