Investigator-Initiated vs. Investigator-Sponsored Research: Definitions Matter
Bibliographic record
Abstract
To the EditorInvestigator-initiated trials (IITs), investigator-initiated studies (IISs), investigator-sponsored trials (ISTs), investigator-initiated research (IIR), non-registration trials (NRTs), non-sponsored trials, academic clinical trials, physician-led studies, investigatordriven clinical trials, and academic studies are only some of the terms used to describe research developed at the "site" level for which an individual, most often the principal investigator, is the one who conceives of and develops a research protocol, with or without support.Though these terms are often used interchangeably, they do not always describe the same concept.This lack of clarity ought to be addressed and we suggest that a single term: investigator-initiated (II) be employed to describe these situations.Issues are further perplexed when trying to identify the funding source based on the term used.To begin, consider the second part of the aforementioned terms.The term "trials", as opposed to "studies" or "research", specifies the type of a project.If accurately used, it should reflect actual clinical trials as per the regulatory definition, i.e. interventional studies [1] as opposed to "studies" that include both interventional and observational projects.The term "research", often a synonym of "studies", is also used to include all types of projects and can be used as the "umbrella" term to ensure the inclusion of any type of study.The most commonly used terms in this space are investigator-initiated and investigator-sponsored.Based on a Pub-Med [2] search, the term "investigator-initiated" appeared for the first time in a published (PubMed indexed) paper in 1979 and was infrequently used until 2010, while the less used term "investigator-sponsored" appeared in 1987 and was not integrated into the mainstream terminology until 2015 -2016.Though some use these terms are considered by many to be synonymous or refer to the same concept, we argue that "initiated" and "sponsored" capture different Financial
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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 | 0.232 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".