Methanol toxicity outbreak: when fear of COVID-19 goes viral
Bibliographic record
Abstract
<h3>Abstract</h3> <h3>Objective</h3> To characterize the causes of marked elevation of C-reactive protein (CRP) levels, investigate patient outcomes, and examine factors that might influence the CRP response. <h3>Design</h3> Health records were used to retrospectively determine patient characteristics, diagnoses, and outcomes over a 2-year period (2012 to 2013). <h3>Setting</h3> A large referral centre in Moncton, NB. <h3>Participants</h3> Adult inpatients and outpatients with a CRP level above 100 mg/L. <h3>Main outcome measures</h3> Differences among the CRP distributions of various diagnosis categories were examined using Kruskal-Wallis tests, and factors affecting outcomes were examined using Fisher exact tests. <h3>Results</h3> Over the 2-year period, 1260 CRP levels (839 patients; 3.1% of all tests) were above 100 mg/L (range 100.1 to 576.0 mg/L). The mean age was 63 years (range 18 to 101) and 50.2% of patients were men. Infection was the most prevalent cause (55.1%), followed by rheumatologic diseases (7.5%), multiple causes (5.6%), other inflammatory conditions (5.4%), malignancy (5.1%), drug reactions (1.7%), and other conditions (2.0%). A diagnosis could not be established in 17.6% of cases. On average, infections caused higher peak CRP levels (<i>W</i> = 34 519, <i>P</i> < .001) and infection was present in 88.9% of cases with CRP levels greater than 350 mg/L. Rheumatologic causes were associated with only 5.6% of CRP levels above 250 mg/L. The overall mortality was 8.6% and was higher in patients with malignancy (37.0%), multiple diagnoses (21.0%), and leukopenia (20.7%, <i>P</i> = .002). <h3>Conclusion</h3> Most patients had infections and the proportion of patients with infections increased with the level of CRP, although many diagnoses were associated with markedly elevated CRP levels. These data could help guide health care professionals in the evaluation and management of these patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.037 | 0.000 |
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 teacher head, 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".