Redefining true leukocytosis in the traumatic lumbar puncture
Bibliographic record
Abstract
Objective: To compare and contrast the observed versus predicted number of white blood cells (WBCs) in a traumatic cerebrospinal fluid (CSF) sample in children and adults. Background: Clinicians rely on a correction formula (Predicted_CSF_WBC=CSF_RBC×Blood_WBC/Blood_RBC) to determine if a true CSF leukocytosis exists. This formula may overestimate true CSF leukocytosis and lead to delayed treatment of meningitis. Methods: A retrospective review of CSF data of 105 patients who met the following criteria: 1) CSF from lumbar puncture (LP) contained≥1000 RBC/mm^3 and 2) CBC performed≤24 hours of LP; 3) negative CSF cultures. Regression analysis was performed to determine the relationship between actual and predicted CSF WBC values. Results: Regression modeling indicated a discrepancy in the predicted versus actual WBC values. Mean adult age was 48.9 years; CSF profile (mean WBC 146.3×10^6/L; RBC 17374×10^6/L; glucose 4.1 mmol/L; protein 1.4 g/L); mean peripheral WBC was 8.2×10^9/L; RBC 3.9×10^9/L. Mean pediatric age was 1.4 years; CSF profile (mean WBC 171.8x10^6/L; RBC 41763x10^6/L; glucose 2.7 mmol/L; protein 1.7 g/L); mean peripheral WBC was 12×10^9/L; RBC 7.2×10^9/L. Observed LP CSF WBC value was 47% of predicted (r^2=0.54 pediatric cohort; r^2=0.91 adults). Conclusion: True CSF leukocytosis could be missed in a traumatic CSF sample based on a currently applied correction formula. We propose the following modifcation: Observed_CSF_WBC=0.5x[CSF_RBC×Blood_WBC/Blood_RBC].
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".