AutoReporter: Development of an artificial intelligence tool for automated assessment of research reporting guideline adherence
Bibliographic record
Abstract
Abstract Objective To develop AutoReporter, a large-language-model system that automates evaluation of adherence to research reporting guidelines. Materials and Methods Eight prompt-engineering and retrieval strategies coupled with reasoning and general-purpose LLMs were benchmarked on the SPIRIT-CONSORT-TM corpus. The top-performing approach, AutoReporter, was validated on BenchReport, a novel benchmark dataset of expert-rated reporting guideline assessments from 10 systematic reviews. Results AutoReporter, a zerolZlshot, nolZlretrieval prompt coupled with the o3-mini reasoning LLM, demonstrated optimal accuracy (CONSORT: 90.09%; SPIRIT: 92.07%), run-time (CONSORT: 617.26 seconds; SPIRIT: 544.51 seconds), and cost (CONSORT: 0.68 USD; SPIRIT: 0.65 USD). AutoReporter achieved a mean accuracy of 91.8% and substantial agreement (Cohen’s κ>0.6) with expert ratings from the BenchReport benchmark. Discussion Structured prompting alone can match or exceed fine-tuned domain models while forgoing manually annotated corpora and computationally intensive training. Conclusion LLMs can feasibly automate reporting guideline adherence assessments for scalable quality control in scientific research reporting. AutoReporter is publicly accessible at https://autoreporter.streamlit.app .
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How this classification was reachedexpand
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".