Methanol toxicity outbreak: when fear of COVID-19 goes viral
Bibliographic record
Abstract
Abstract Objective 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. Design Health records were used to retrospectively determine patient characteristics, diagnoses, and outcomes over a 2-year period (2012 to 2013). Setting A large referral centre in Moncton, NB. Participants Adult inpatients and outpatients with a CRP level above 100 mg/L. Main outcome measures 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. Results 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 (W = 34 519, P < .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%, P = .002). Conclusion 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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".