ChatGPT assists in identifying and recommending research study designs to support study protocol writing
Bibliographic record
Abstract
This study aimed to validate ChatGPT by identifying the types of study designs in the published scientific literature and recommending the types of research study designs. ChatGPT Version 3.5 was asked to classify the published literature into different study designs. ChatGPT answers were then compared to the blinded independent reviewers' findings. The ChatGPT was then used to provide recommendations for study designs with the research titles from 333 unpublished study protocols. A total of 463 articles were used for validation. The Kappa coefficient was 84.45% agreement for the published literature. The agreement between ChatGPT and reviewers based on whole study protocol was 56.2%. ChatGPT may assist in classifying the types of study design with reasonably good accuracy if an abstract or full text is provided. ChatGPT may also be useful in recommending study designs to a novice researcher but with oversight from experts.
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 | 0.562 | 0.769 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.030 | 0.015 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.027 |
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".