Reporting guidelines for chatbot health advice studies: explanation and elaboration for the Chatbot Assessment Reporting Tool (CHART)
Bibliographic record
Abstract
The Chatbot Assessment Reporting Tool (CHART) reporting guideline promotes transparent and comprehensive reporting of studies evaluating the performance of generative artificial intelligence (AI)-driven chatbots for the purposes of summarising clinical evidence and providing health advice, referred to here as chatbot health advice (CHA) studies. CHART is the product of an international, multi-phase, consensus based initiative involving various stakeholders and comprises a 12-item checklist with 39 subitems. The checklist includes items on open science, title and abstract, introduction, model identification, model details, prompt engineering, query strategy, performance definition and evaluation, statistical analysis, results, discussion, with an accompanying flow diagram. Each item includes distinct subitems. This explanation and elaboration article discusses each subitem and provides a detailed rationale for its inclusion in the CHART checklist.
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 | 0.287 | 0.562 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.033 | 0.024 |
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".