Neurotological findings after electrical injury at the workplace
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Neurotological findings secondary to electrical injuries have rarely been reported in the world literature. We attempt to characterize the neurotological findings following electrical injury and to determine the role head injury and loss of consciousness play in this population's clinical presentation. STUDY DESIGN: Retrospective cohort study. METHODS: A database containing 3,438 patients with work-related injuries was scanned for individuals who sustained and survived electrical injuries at work. Detailed analysis of the frequencies of presenting features and test results was performed. A comparative analysis was made between the subsets of patients with and without loss of consciousness and/or head injury. RESULTS: A cohort of 42 patients was identified. All patients had multiple symptoms. Dizziness was a significant complaint in all workers with electrical injuries. Other common complaints included tinnitus and imbalance. Characterization of these symptoms is provided in detail according to statistical frequency. In this cohort, 25 workers had a concomitant head injury and 17 workers had an associated loss of consciousness. There was no statistically significant difference when clinical presentation, examination, and balance testing results were compared between the subsets. CONCLUSIONS: Frequency and characterization of symptoms following electrical injury are provided. Dizziness is the most common presenting neurotological feature. Loss of consciousness and/or associated head injury do not affect the clinical presentation in this particular population. LEVEL OF EVIDENCE: 2b. Laryngoscope, 127:2126-2131, 2017.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".