Abstract 117: Application Of A Delphi Method To Develop A Patient Decision Aid For Implantable Cardioverter Defibrillator Candidates
Bibliographic record
Abstract
Background: Development of patient decision aids (PtDA) requires decision making about the facts, risks, and benefits to present to patients. Delphi process methodology has been employed successfully as a consensus-building tool across several disciplines. This work represents the first phase of a Canadian study that is developing a PtDA for prophylactic implantable cardioverter defibrillator (ICD) candidates. Our aim was to engage healthcare providers, ICD stakeholders, and patients in decisions about the content and format of the PtDA, using a modified Delphi process. Method: Twenty healthcare professionals, 1 stakeholder representative, and 16 people living with ICDs were invited to participate. Our Delphi approach utilized electronic and paper based response systems that included 1) anonymity, 2) iteration, 3) controlled feedback and, 4) statistical group response options. The first Delphi round comprised 39 evidence-based survey items including ICD facts, risks, benefits, and values. A review panel of decision aid experts, cardiovascular researchers, and electrophysiologists selected the content items. Participants completed the survey by rating each category item on a 5-point likert scale from “1= not important” to “5 = extremely important”. A predetermined cut-off of > 75%, wherein participants rated an item as “very important” or higher, guided the selection of items during the two Delphi rounds completed. To provide participants with statistical group responses, the second round participants received the anonymous first round item rankings prior to rating the 11 remaining items. Results: Twenty-seven participants completed round one of the Delphi (14 ICD patients). Mean (SD) age of ICD patients was 66.8 (7.1) years. The number of years of experience in healthcare reported by non-ICD respondents ranged from 2 to30 years. Of the 39 items included in round one, 14 items were retained ( > 75% agreement), 14 items were removed (<60% agreement), and 11 items proceeded to the second round (60-74% agreement). During the second Delphi round (n=27), 6 of the 11 items that did not reach agreement in round one were retained ( > 75% agreement). Examples from item categories include: 1) Risk items - i ) Lead problems over the long term, ii ) Possible complications during the surgical procedure to insert the ICD. 2) Benefit items- i ) Prevent cardiac arrest from a dangerous heart rhythm, ii ) Assurance that a dangerous heart rhythm can be corrected. 3) Fact, Value & Preferences - i ) What a standard ICD cannot do, ii ) The wish for a natural death, iii ) Driving restrictions if the ICD delivers a shock. Conclusion: The results from this Delphi survey informed the content that will be incorporated into a PtDA for new ICD candidates. The next phase will include field-testing of the PtDA. Ultimately, the goal is to support quality decision making in ICD candidates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".