© 2014 Canadian Medical Association or its licensors CMAJ 1
Bibliographic record
Abstract
An 81-year-old man with a history of cor onary artery disease experienced a sudden choking feeling, sore throat, difficulty swallowing and itchy mouth immedi-ately after finishing dinner. He was taking clo-pidogrel 75 mg combined with 100 mg acetyl-salicylic acid once daily, and metoprolol tartrate 50 mg twice daily for secondary prevention fol-lowing stent implantation 12 months earlier. On examination in the emergency department, a round dark-coloured structure resembling a for-eign body was observed at the back of his mouth. Laryngoscopy identified an enlarged and bruised uvula (Figure 1). Hematocrit, plate-let count and activated thromboplastin time were normal. We diagnosed uvula hematoma, and advised the patient to stop antiplatelet treat-ment and to avoid swallowing hard food. The hematoma disappeared gradually within three weeks, and there was no recurrence after he started monotherapy with acetylsalicylic acid. Dual antiplatelet therapy has been associ-ated with bleeding in 3 % of patients during the first six months of treatment and in 5.5 % of patients by the end of the first year.1 In a large retrospective cohort study, the rate of major bleeding at 30 days of treatment, defined as intracranial bleeding or bleeding associated with a hematocrit decrease of at least 15%, was estimated at 14.3 per 100 person-years.2 Risk factors for major bleeds include age greater than 75 years, liver disease, gastrointestinal disease and prior stroke.3 The most common sites of bleeding are gastrointestinal, intracra-nial and urinary.4 In some cases, treatment may require intravenous fluids, transfusion, anti-fibrinolytics and, rarely, surgical or endoscopic procedures.4 Caution is warranted in stopping both antiplatelet agents, because this has been associated with an increase in major cardiovas-cular events.4 Uvula hematoma has been previously de scribed in relation to streptokinase therapy and endotracheal intubation.5 Our patient’s hema-toma could have been precipitated by trauma related to eating hard crackers.
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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 | 0.001 | 0.010 |
| 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 | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.755 | 0.606 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".