Upper extremity dysfunctions in patients with cardiac implantable electronic devices: a systematic review
Bibliographic record
Abstract
Background/Aims The prevalence of cardiac implantable electronic devices has risen considerably during recent years. This has revolutionised the treatment of cardiac arrhythmias, which in turn reduced the incidence of sudden cardiac death. There are several complications associated with cardiac implantable electronic device implantation. Upper extremity dysfunction is one of the complications associated with this procedure and it should be addressed, since it can affect activities of daily living. This systematic review analysed the prevailing evidence pertaining to the common upper extremity dysfunctions associated with receiving a cardiac implantable electronic device. Methods A comprehensive literature search was conducted using PubMed, Cochrane Central Register of Controlled Trials, EMBASE (through Cochrane) and Google Scholar for original research published in the English language. The Rayyan QCRI web application was used for study selection and the decision-making process. PRISMA guidelines were used to conduct and report this review. The methodological quality of the included studies was appraised using the Newcastle–Ottawa Scale and Joanna Briggs Institute critical appraisal tool for analytic cross-sectional studies and case reports. Results This systematic review included eight studies with a total of 696 participants. The most common upper extremity dysfunctions associated with receiving a cardiac implantable electronic device were pain and limitation of shoulder range of motion. Limitation of range of motion could be persisted even in the long term after having a cardiac implantable electronic device implanted. Conclusions Contributing factors of upper extremity dysfunctions included size of the device, pectoral site of implantation and upper extremity immobilisation practices. Upper extremity dysfunction is an overlooked complication, as it received a relatively low priority, although it may negatively impact quality of life.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".