Overwhelming Pulmonary Infection after a Tobogganing Accident
Bibliographic record
Abstract
A 17‐year‐old male patient presented to St Joseph′s Healthcare (Hamilton, Ontario) with a radiologically opacified left hemithorax. Three days earlier, the patient had injured his left lower chest while tobogganing on his farm. He developed dyspnea and felt unwell, but only sought medical attention from his family doctor a few days after the injury, when fever and pleuritic chest pain ensued. He was treated with a nonsteroidal anti‐inflammatory agent, but his chest radiograph revealed an opacified hemithorax, for which he was referred to the hospital. In the emergency department, the patient looked ill and was in distress. His heart rate was 125 beats/min, and he had a blood pressure of 103/61 mmHg, a respiratory rate of 20 breaths/min, a temperature of 38.5°C and an oxygen saturation of 94% on ambient air. Laboratory results showed a white blood cell count of 40×109/L with a left shift. Chest radiography showed a left pleural effusion. A #28 Fr chest tube was inserted into the left hemithorax, and foul‐smelling serosanguineous fluid was drained. There was a transient improvement of tachypnea and hypoxemia despite minimal radiographic change. He was admitted and subsequently started on intravenous levofloxacin. Overnight, he deteriorated and required an increase in supplemental oxygen. A computed tomography (CT) scan of his chest revealed multiple loculated fluid collections and bilateral pulmonary parenchymal infiltrates consistent with a pneumonia and empyema.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".