P.019 A shared decision-model toolkit for pregnancy related care in neurology
Bibliographic record
Abstract
Background: Shared decision-making (SDM) is a dynamic, patient-engaged approach to collaborative medical care. Limited SDM tools exist in pregnancy. We aimed to examine the need and usability of a novel SDM tool for pharmaco-therapeutic treatment of neurological conditions in pregnancy. Methods: This is an exploratory mixed-methods study. Non-pregnant women of any age were recruited using convenience, purposive sampling from an academic neurology clinic in Toronto. Participants reported the user friendliness of the SDM by completing the systems usability (SUS) questionnaire and participated in a focus group to further elaborate on their experience. Results: Eleven participants completed the survey 45% each between age 31-40, and 51-60. Median time spent on the tool was 17.2 minutes, and median SUS score 70 (<68 being not usable). Thematic data analysis from 2 focus groups, identified technical and content improvements: use of inclusive language, simplified design, and importance of patient engagement in SDM. Conclusions: Based on our preliminary results, a SDM web-tool for medication-related concerns of pregnant patients with neurological conditions is needed and usable. With integration of patients’ lived experiences, this novel tool may serve as an anchor point for future work in this field.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".