Spontaneous Cervical Epidural Hematoma Following COVID-19 Illness Presenting to a Chiropractor: A Case Report
Bibliographic record
Abstract
Cervical epidural hematoma (CEH) is a rare and potentially fatal condition in which blood accumulates in the epidural space of the cervical spine. A 64-year-old man presented to a chiropractor with a two-week history of sudden-onset neck pain, shoulder pain, occipital headache, and numbness in the shoulders and upper extremities. He had recovered from a mild course of coronavirus disease 2019 (COVID-19) illness one month prior. The patient's primary care provider had previously prescribed a nonsteroidal anti-inflammatory drug for his neck pain. However, his symptoms worsened, and he visited the emergency department where he had unremarkable cervical spine radiographs and was discharged with a diagnosis of neck strain. The chiropractor ordered cervical spine magnetic resonance imaging (MRI), revealing a ventral CEH extending from C2 to C5. The chiropractor referred the patient to a nearby hospital for urgent management. The patient was admitted and observed, progressively improved, and did not require surgery. After 10 weeks in the hospital the patient was asymptomatic, a follow-up MRI revealed resolution of the CEH, and the patient was discharged. While the current case highlights a temporal relationship between COVID-19 and CEH, further research is needed to determine if COVID-19 is a risk factor for this condition. Clinicians who encounter patients with spinal disorders must be able to recognize the clinical features of CEH and refer these patients for emergency care and/or neurosurgical evaluation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".