Production of Evidence-Based Informed Consent (EBIC) With Meaning Equivalence Reusable Learning Objects (MERLO)
Bibliographic record
Abstract
Apparently, during an informed consent, patients remember little of the information given and their comprehension level is often overestimated by physicians. This study measures level of understanding of informed consent for elective cesarean surgery using an evidence-based informed consent (EBIC) model based on six MERLO assessments. MERLO recognition and production scores and follow-up interviews of 50 patients and their partners were recorded. Statistical comparison of scores within couples was performed by weighted kappa agreement, t-tests, and Ward's hierarchical clustering. Recognition score means were high for patients and partners with low standard deviation (SD), while production scores means were lower with higher SD. Clustering analysis showed that only 70% (35/50) of couples were assigned to the same cluster and t-test yields significant difference of scores within couple. Kappa yields moderate agreement levels on all items except for items D and C, which are lower. Follow-up interviews show that participants consider MERLO assessments to be helpful in improving comprehension.
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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 | 0.024 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".