Electrodiagnostic reporting preferences of referring physicians: An exploratory survey
Bibliographic record
Abstract
INTRODUCTION/AIMS: Electrodiagnostic (EDX) studies play a crucial role in the evaluation of patients with peripheral nervous system disorders. Accurate and succinct communication of test results is critical to patient safety and clinical decision-making. The objective of this study was to explore EDX reporting preferences of referring physicians to improve quality of communication and patient care. METHODS: An online survey was developed, and a purposive sampling strategy was used to recruit physicians in the authors' professional networks. Quantitative and qualitative survey data underwent frequency and thematic analyses, respectively. RESULTS: There were 40 respondents, including: 21 non-surgical specialists, 12 surgical specialists, and 7 family physicians. Sections rated as most critical were diagnostic impression (97%) and summary/interpretation (72%). Only 18% reported numeric data as critical to their needs, preferring this data to be formatted as bullet points or tables without nerve conduction study waveforms. Regarding the format of the data summary and diagnostic impression sections, the majority of respondents preferred bullet points rather than paragraphs. DISCUSSION: The results of this exploratory survey suggest that physicians who refer patients for EDX studies prefer reports that emphasize the interpretation of EDX data and a clear diagnostic impression, particularly in bullet point format. This project highlights important preferences and how they compare to recommended reporting guidelines, which may help improve communication and ultimately patient care. Future efforts should explore larger sample sizes with all key stakeholders in the EDX process to better understand reporting styles and preferences with greater nuance and context.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".