급성 두통환자의 거미막밑출혈 예측을 위한 호중구/림프구 비율 및 임상 예측 지표방법의 유용성
Bibliographic record
Abstract
Objective: This study evaluated the clinical usefulness of the neutrophil-lymphocyte ratio (NLR), Ottawa subarachnoid hemorrhage (SAH) rule and EMERALD (Emergency Medicine, Registry Analysis, Learning and Diagnosis) SAH rule for predicting SAH in patients with acute headache. Methods: This clinical retrospective study was conducted at an urban emergency department between January 2008 and December 2017. Alert, neurologically intact adult patients with acute headache were included. All data were drawn from electrical medical charts. The Ottawa SAH rule (positive if any of age ≥40, neck pain, loss of consciousness, onset during exertion, thunderclap headache, and neck stiffness), EMERALD SAH rule (positive if any of systolic blood pressure >150 mmHg, diastolic blood pressure >90 mmHg, serum glucose >115 mg/dL, or serum potassium < 3.9 mEq/L) and NLR were assessed. The sensitivity and specificity of these tools for detecting or ruling out SAH was calculated. Results: Among the 1,230 patients enrolled in this study, 299 (24.3%) were diagnosed with SAH. To predict SAH, the Ottawa SAH rule offered 100% sensitivity but 31.6% specificity. Applying the EMERALD SAH rule to patients positive for the Ottawa SAH rule led to 92.6% sensitivity and 48.0% specificity. As the NLR alone showed less efficacy with the area under curve of 0.696 by receiver operating analysis, NLR ( >2.1) was added to the last step to have achieve 99.0% sensitivity and 56.7% specificity. Conclusion: The stepwise application of the Ottawa, EMERALD SAH rule, and NLR increased the specificity compared to each application. On the other hand, further studies will be needed to increase the sensitivity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.011 |
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; both teacher heads 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".