Cerebrospinal Fluid Total Protein Reference Intervals Derived from 20 Years of Patient Data
Bibliographic record
Abstract
Abstract BACKGROUND Reference intervals are vital for interpretation of laboratory results. Many existing reference intervals for cerebrospinal fluid total protein (CSF-TP) are derived from old literature because of the invasive nature of sampling. The objective of this study was to determine reference intervals for CSF-TP using available patient data. METHODS Twenty years of hospital database information was mined for previously reported CSF-TP results. Associated demographic, laboratory, and clinical diagnosis (International Classification of Diseases 9/10 codes) details were extracted. CSF-TP results included 3 different analytical platforms: the Siemens Vista 1500, Beckman Lx20, and Roche Hitachi 917. From an initial data set of 19591 samples, the following exclusion criteria were applied: incomplete data, white blood cells (WBCs) >5 × 106/L, red blood cells (RBCs) >50 × 106/L, and glucose <2.5 mmol/L. Patient charts were reviewed in detail to exclude 60 different conditions for which increases in CSF-TP would be expected. A total of 6068 samples were included; 63% of the samples were from females. Continuous reference intervals were determined using quantile regression. Age- and sex-partitioned intervals were established using the quantile regression equation and splitting age-groups into 5-year bins. RESULTS CSF-TP showed a marked age dependence, and males had significantly higher CSF-TP than females across all ages. CSF-TP results from the 3 different instruments and manufacturers showed small (approximately 0.04 g/L), but statistically significant, differences. CSF-TP showed weak, but again statistically significant, correlation with WBC and RBC but was independent of serum total protein and creatinine. CONCLUSIONS The age dependence of CSF-TP supports that age-partitioned reference intervals will be more accurate than a single cutoff, particularly in patients with advancing age.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".