Psychological effects of implantable cardioverter defibrillator shocks. A review of study methods
Bibliographic record
Abstract
BACKGROUND: The implantable cardioverter defibrillator (ICD) saves lives but clinical experience suggests that it may have detrimental effects on mental health. The ICD shock has been largely blamed as the main offender but empirical evidence is not consistent, perhaps because of methodological differences across studies. OBJECTIVE: To appraise methodologies of studies that assessed the psychological effects of ICD shock and explore associations between methods and results. DATA SOURCES: A comprehensive search of English articles that were published between 1980 and 30 June 2013 was applied to the following electronic databases: PubMed, EMBASE, NHS HTA database, PsycINFO, Sciencedirect and CINAHL. REVIEW METHODS: Only studies testing the effects of ICD shock on psychological and quality of life outcomes were included. Data were extracted according to a PICOS pre-defined sheet including methods and study quality indicators. RESULTS: Fifty-four observational studies and six randomized controlled trials met the inclusion criteria. Multiple differences in methods that were used to test the psychological effects of ICD shock were found across them. No significant association with results was observed. CONCLUSIONS: Methodological heterogeneity of study methods is too wide and limits any quantitative attempt to account for the mixed findings. Well-built and standardized research is urgently needed.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".