“Piece by Piece” understanding of patient reported QOL and EV‐ICD: Response to letter from Vicentini and Rodorf
Bibliographic record
Abstract
Vincentini and Rodorf highlight some additional points of interest related to our use of their subcutaneous ICD (S-ICD) patient sample as a comparison to the extra vascular implantalbe cardioverer defibillator (EV-ICD) patient group. As noted,1 we selected their study to use the historical norms on the Florida Patient Acceptance Survey2 comparisons as we believed that they were the most representative sample available in the literature. However, as with any historical comparison data set, important differences should be considered in interpretations, including differences in timepoint of patient reported outcomes (PROs) assessment (EV-ICD at 6 months vs. S-ICD at 12 months), composition of HFrEF versus non-HFrEF, age range, and BMI range. Each of these differences warrants consideration, but we suggest that the two most important of these differences related to PROs are the timing of assessment and average age of the sample. The primary purpose of PROs is to understand the patient experience broadly to refine care processes, increase patient acceptance, and improve health outcomes across time. At what point is the right time to evaluate a technology? The timing of PROs assessment does not garner much attention overall in the literature. Novel technologies produce a “wow” factor but are tempered if a poor patient experience results. Optimal assessment should sample the many domains of the patient experience with an orientation comparable to quality initiatives that are ongoing, recursive, and consistent in adapting processes to produce patient benefit. Clearly, the “acute phase of adjustment” (first 3 months) to an implantation or surgical intervention focuses more on pain and discomfort and possibly less on functional outcomes and psychological adjustment. However, we would suggest that “mid-range adjustment” (6–12 months) begins a rational period to sample the patient experience (Figure 1). Regular and ongoing assessment of the patient experience, at least annually, should also be strongly considered in research and in practice. The cardiac disease course changes and common psychosocial challenges present themselves, leading to changing evaluations by the patient. We agree that our use of the 6-month assessment likely produces slightly different samples of experience from the 12-month, but both have utility in evaluating patient experience and should be ongoing prospectively. Finally, the age of patients considering S-ICD and EV-ICD remains a point of interest and emphasis. The psychological adjustment of patients less than 50 years of age has been long been a point from our research.3 The psychosocial sequalae and disease presentation and course can be quite different than our older and more typical device patient cohorts4; both samples discussed here had relatively younger average ages (EV-ICD: 53 years vs. S-ICD: 56 years). These lower aged cohorts can likely be explained by the desire to prevent long term lead use in relatively young patients, but it also will magnify the potential importance of the patient experience and psychosocial impacts. Collectively, these samples may rate the patient experience “harder” because they have more challenges and higher expectations as they compare their experience to same age nondevice persons. Clearly, ongoing innovation and care planning that minimizes impact on lifestyle and optimizes the patient experience should be continuously rigorously pursued. We remain in agreement and acknowledge that PROs create value by providing a “big piece” of the unfolding story for technology assessment and innovation. Future research fills in the picture “piece by piece” with data.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.025 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 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".