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Record W4390584235 · doi:10.1111/jce.16175

Patient reported QOL and EV‐ICD: Response to letter from Kataoka and Imamura (2024)

2024· letter· en· W4390584235 on OpenAlexaff
Samuel F. Sears, Rebecca Harrell, Ian Crozier, Francis Murgatroyd, Lucas V.A. Boersma, Jaimie Manlucu, Bradley P. Knight, Christophe Leclercq, Ulrika Birgersdotter‐Green, Christopher Wiggenhorn, Gregory Hilleren, Paul A. Friedman

Bibliographic record

VenueJournal of Cardiovascular Electrophysiology · 2024
Typeletter
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsSouth carolinaMedicineGerontologyLibrary sciencePolitical science

Abstract

fetched live from OpenAlex

Our authorship group appreciates the careful consideration of the key issues related to the patient experience posited by Kataoka and Imamura.1 Following publishing of the efficacy and safety data of the extravascular implantable cardioverter defibrillator (EV-ICD),2 our study provided the first available patient reported outcomes (PROs) using both generic and device-specific quality of life (QOL) metrics, and we acknowledge that many questions remain to refine our understanding of the patient experience. Kataoka and Imamura1 queried possible additional group differences in PROs. First, the issue of primary versus secondary prevention indication for implantation (81.8% were primary prevention) and subsequent exposure to ICD shocks was questioned. We compared primary versus secondary indication on the physical and mental subscales of the SF-12 and the composite and subscales of the FPAS, and no differences were found (>0.05). Comparisons between groups with shock and no shock also did not show any differences. Collectively, the existing data does not suggest that device indication or shock exposure impacted PROs. The role of body mass index (BMI) was also raised as a particularly relevant limitation in our study. The average BMI for the full efficacy sample was 28.0 ± 5.6 kg/m2 and our subsamples were 27.8 and 29.3 kg/m2 (completed both SF-12 surveys or completed the FPAS survey, respectively). Kataoka and Imamura question whether there was equitable QOL outcomes by patients with smaller physiques with possible considerations for Asian populations. As we reported,3 BMI scores were significantly different with the smallest BMI group (BMI < 25) reporting a lower mental score on average. Moreover, body image concerns were the highest in the lowest BMI group (BMI < 25) and the youngest (age < 50 years). In contrast, previous reviews with subcutaneous ICDs (S-ICD) in pediatric populations4 has suggested only minor implant modifications may need to be considered, and Vincentini et al. did not find any differences in device acceptance by body habitus.5 The size of the EV-ICD may also be a consideration for shared decision making with respect to the decision of which ICD to implant in which patient. Specifically, the EV-ICD compares favorably in size to the S-ICD (33 vs. 60 cm3, respectively). Device related distress differed between groups and the size of the device may be a factor. The subscale of Device-Related Distress directly queries feelings of disfigurement from the device and this may account for the differences. Finally, the question was raised of selection bias related to the possibility of patients at risk for decreased QOL or patients with smaller body habitus leading to possible avoidance of being enrolled in this study. We do not have any data to refute or accept this limitation as this is the initial sample of patients considering EV-ICDs. However, as noted in the paper,3 we do acknowledge that the process of agreeing to a novel device may induce a degree of positive acceptance bias in patients and in their reporting of outcomes. As a result, we indicated that cautious interpretation of this data is needed until full randomized studies could disentangle these common psychological processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.015
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0150.011
Insufficient payload (model declined to judge)0.0110.004

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.

Opus teacher head0.010
GPT teacher head0.246
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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