Cannabinoid Hyperemesis Syndrome Masquerading as Uremia: An Educational Case Report
Bibliographic record
Abstract
RATIONALE: With marijuana legalization, clinicians need to be aware of Cannabinoid Hyperemesis Syndrome (CHS), which may masquerade as other disease states such as uremia. PRESENTING CONCERNS OF THE PATIENT: A 37-year-old man with bipolar affective disease treated with lithium had progressive renal insufficiency presumably on the basis of interstitial fibrosis. He developed persistent and severe nausea and vomiting which was assumed to be on the basis of uremia. Predating the nausea and vomiting was a history of daily marijuana use. DIAGNOSES: Renal insufficiency, bipolar affective disease, and intractable nausea and vomiting. INTERVENTIONS: Dialysis was initiated but did not improve his symptoms and multiple investigations revealed no other cause. Abstinence from marijuana use resulted in complete resolution of symptoms. OUTCOMES: The patient elected to discontinue dialysis and was still alive 7 months later. We concluded the nausea and vomiting were not on a uremic basis but more likely due to CHS. LESSONS LEARNED: With more widespread use of marijuana, it is important to be aware of CHS, which may be confused with uremia in patients with concomitant renal insufficiency.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".