Presentation of an 85-Year-Old Woman With Musculoskeletal Pain to a Chiropractic Clinic: A Case of Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVE: The purpose of this case is to describe a patient who had a stroke preceding a chiropractic appointment and was unaware that the cerebrovascular event had occurred. CLINICAL FEATURES: An 85-year-old established patient presented for chiropractic treatment of pain in the left side of the neck, hip, and low back associated with known advanced degenerative spinal disease and lumbar stenosis. On the day of presentation, the patient reported morning nausea, double vision, and right-sided vision loss; she related that she had collided into a car while driving to the appointment. Review of her medical history divulged residual neurological deficits related to a previous subdural hematoma, resulting in craniotomy. Examination revealed a right inferior quadrantanopia in the right eye and right nasal hemianopia in the left eye. Nystagmus was present in the left eye with saccadic intrusion on pursuit right to left. INTERVENTION AND OUTCOME: The patient was transported immediately to an emergency room,where diagnosis of an Acute infarct in the left cerebrum at the junction of the left occipital, parietal and temporal lobes in the watershed area was confirmed. CONCLUSION: Patients with signs and symptoms of stroke in progress may occasionally present for chiropractic care. It is imperative to complete a thorough history and examination prior to care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".