Development and Acceptability Testing of a Patient Decision Aid on Levodopa Intestinal Gel for Parkinson Disease
Bibliographic record
Abstract
Background Parkinson’s disease (PD) is a neurodegenerative disease characterized by motor and nonmotor symptoms that worsen over time. In some cases, an advanced treatment may be needed. The use of levodopa-carbidopa intestinal gel (LCIG) is one of these options. However, deciding whether to receive it can be difficult. A patient decision aid (PDA), a tool designed to inform about treatment options, can help and promote patients’ participation in decision making. Objectives This study was conducted to develop a PDA on LCIG and assess its acceptability. Methods The International Patient Decision Aid Standards framework was used to develop the PDA. An advisory committee ( n = 5) gave feedback on the PDA prototype. Acceptability was evaluated using a cross-sectional descriptive design. A convenience sample of 36 participants (including persons with PD receiving and not receiving LCIG, caregivers, and health care professionals) was used. Acceptability data, sociodemographics, and health literacy were collected using questionnaires and a focus group. Results Sample characteristics were a mean age of 64.4 y ( s = 14 y), university level of education (46.7%), and duration of illness of less than 10 y (80%). The health literacy score was judged as very good ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mrow> <mml:mover> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>¯</mml:mo> </mml:mover> </mml:mrow> </mml:math> = 55.2/70, s = 7.3). Qualitative data analysis allowed for final adjustments to the published PDA version. Conclusions. This study is the first to report the development of a PDA on LCIG and its acceptability testing. Participants found the PDA to be useful and would recommend it. All health care professionals indicated they intended to use it in their practice. More research will be needed to evaluate the PDA’s implementation and its effects on users. Highlights Deciding whether to opt for an advanced Parkinson’s disease treatment such as levodopa-carbidopa intestinal gel can be challenging for many patients. A patient decision aid on levodopa-carbidopa intestinal gel for Parkinson’s disease persons, caregivers, and health care professionals was developed to support a decision-making process. This article summarizes the steps used for its development and its acceptability testing.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".