Evaluating the validity of <scp>ChatGPT</scp> responses on common obstetric issues: Potential clinical applications and implications
Bibliographic record
Abstract
OBJECTIVE: To evaluate the quality of ChatGPT responses to common issues in obstetrics and assess its ability to provide reliable responses to pregnant individuals. The study aimed to examine the responses based on expert opinions using predetermined criteria, including "accuracy," "completeness," and "safety." METHODS: We curated 15 common and potentially clinically significant questions that pregnant women are asking. Two native English-speaking women were asked to reframe the questions in their own words, and we employed the ChatGPT language model to generate responses to the questions. To evaluate the accuracy, completeness, and safety of the ChatGPT's generated responses, we developed a questionnaire with a scale of 1 to 5 that obstetrics and gynecology experts from different countries were invited to rate accordingly. The ratings were analyzed to evaluate the average level of agreement and percentage of positive ratings (≥4) for each criterion. RESULTS: Of the 42 experts invited, 20 responded to the questionnaire. The combined score for all responses yielded a mean rating of 4, with 75% of responses receiving a positive rating (≥4). While examining specific criteria, the ChatGPT responses were better for the accuracy criterion, with a mean rating of 4.2 and 80% of the questions received a positive rating. The responses scored less for the completeness criterion, with a mean rating of 3.8 and 46.7% of questions received a positive rating. For safety, the mean rating was 3.9 and 53.3% of questions received a positive rating. There was no response with an average negative rating below three. CONCLUSION: This study demonstrates promising results regarding potential use of ChatGPT's in providing accurate responses to obstetric clinical questions posed by pregnant women. However, it is crucial to exercise caution when addressing inquiries concerning the safety of the fetus or the mother.
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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 | 0.163 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), 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".