Patient-oriented research to support decision-making in pregnancy hypertension
Bibliographic record
Abstract
Background: Pregnancy hypertension is a common, potentially fatal condition. New guidance recommends ‘tight’ control of pregnancy hypertension over ‘less-tight’ control. However, guidance also suggests that treatment recommendations consider patient preferences. This dissertation aims to understand how to support patients and providers to make preference-congruent and informed decisions about pregnancy hypertension management. Methods: First, a mixed-methods study, including a best-worst scaling task, of patient preferences for pregnancy hypertension management was conducted. Next, a systematic review built upon ancillary findings by assessing emotion in patient decision aids (PtDAs) for decisions during pregnancy. Using results from the preferences study, the subsequent study re-analyzed the Control of Hypertension in Pregnancy Study (CHIPS) trial using a patient-oriented composite endpoint. A PtDA was developed and assessed for quality and effectiveness. Lastly, a preliminary study explored emotion-regulation in patient decision-making. Results: The mixed-methods preference study (n=210) found that individuals prioritised seven outcomes when choosing how to manage pregnancy hypertension. Latent class analysis identified three preference profiles (a profile comprises participants with similar preferences). Each profile placed different importance on each outcome: 1) ‘equal prioritisers’ valued the outcomes equally; 2) ‘early delivery avoiders’ prioritised avoiding delivery before 34 weeks; and 3) ‘medication minimisers’ prioritised avoiding medication. A systematic review of 39 PtDAs found that most did not address emotion. Reanalysis of the CHIPS trial using a weighted patient-oriented composite endpoint found that while both strategies yielded equal outcomes for equal prioritisers; ‘tight’ control produced better outcomes for early delivery avoiders; and ‘less-tight’ control produced better outcomes for medication minimisers. A prototype PtDA that incorporated these profiles was assessed (n=99) as very acceptable and clear, and significantly improved knowledge. The preliminary emotion study (n=107) found that individuals’ beliefs about their own ability to regulate emotions may limit the benefit of a PtDA. Conclusions: Patient preferences for management of pregnancy hypertension can be broadly described by three profiles. ‘Tight control’ is well-suited to only two of these profiles, emphasizing the importance of shared decision-making in reaching treatment decisions. A PtDA for pregnancy hypertension may help patients make more informed decisions. Future work should explore how to include emotion in PtDAs.
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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 | 0.100 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".