P.040 Redefining true leukocytosis in the traumatic lumbar puncture
Bibliographic record
Abstract
Background: Clinicians rely on a correction formula (Predicted CSFWBC=CSFRBC×BloodWBC/ BloodRBC) to determine if a true CSF leukocytosis exists. This formula may overestimate true CSF leukocytosis leading to delayed diagnosis and treatment of meningitis. Methods: A retrospective review of CSF data of 105 patients registered at 3 hospitals (Saskatoon, Canada) between 2011-2013 who met the following criteria: 1) CSF samples from lumbar puncture (LP) contained≥1000 RBC/mm3; 2) a complete blood count (CBC) performed within 24 hours of LP; and 3) CSF not obtained due to high clinical suspicion of meningitis and was negative for microbial staining and culture. Regression analysis was performed to determine the relationship between actual and predicted CSF WBC values. Results: Mean adult age was 48.9 years; CSF profile (mean WBC 146.3×106/L; RBC 17374×106/L; glucose 4.1 mmol/L; protein 1.4 g/L); mean peripheral WBC 8.2×109/L; RBC 3.9×109/L. Mean pediatric age was 1.4 years; CSF profile (mean WBC 171.8; RBC 41763; glucose 2.7; protein 1.7); mean peripheral WBC 12; RBC 7.2. The observed LP CSF WBC value was 47% of predicted (r2=0.54 pediatric cohort; r2=0.91 adult cohort). Conclusions: True CSF leukocytosis in both pediatric and adult patients could be missed in a traumatic CSF sample if correction is based on current formulas. We propose a modifcation: ObservedCSFWBC=0.5×[CSFRBC×BloodWBC/BloodRBC].
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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 | 0.022 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".