Can Routine Laboratory Tests Discriminate between Severe Acute Respiratory Syndrome and Other Causes of Community‐Acquired Pneumonia?
Bibliographic record
Abstract
BACKGROUND: The clinical presentation of severe acute respiratory syndrome (SARS) resembles that of other etiologies of community-acquired pneumonia, making diagnosis difficult. Hematological and biochemical abnormalities, particularly lymphopenia, are common in patients with SARS. METHODS: With the use of 2 databases, we compared the ability of the absolute lymphocyte count, absolute neutrophil count, lactate dehydrogenase level, creatine kinase level, alanine aminotransferase level, and serum calcium level at hospital admission to discriminate between cases of SARS and cases of community-acquired pneumonia. The SARS database contained data for 144 patients with SARS from the 2003 Toronto SARS outbreak. The community-acquired pneumonia database contained data for 8044 patients with community-acquired pneumonia from Edmonton, Canada. Patients from the SARS database were matched to patients from the community-acquired pneumonia database according to age, and receiver operating characteristic curves were constructed for each laboratory variable. RESULTS: The areas under the receiver operating characteristic curves (AUCs) demonstrated fair to poor discriminatory ability for all laboratory variables tested except absolute neutrophil count, which had an AUC of 0.80, indicating good discriminatory ability (although there was no cutoff value of the absolute neutrophil count at which reasonable sensitivity or specificity could be obtained). Combinations of any 2 tests did not perform significantly better than did the absolute neutrophil count alone. CONCLUSIONS: Routine laboratory tests, including determination of absolute lymphocyte count, should not be used in the diagnosis of SARS or incorporated into current case definitions of SARS. The role of the absolute neutrophil count in SARS diagnosis is likely limited, but it should be assessed further.
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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 | 0.029 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".