Evaluation of the protein gap for detection of abnormal serum gammaglobulin level: an imperfect predictor
Bibliographic record
Abstract
OBJECTIVES: The value of the serum protein gap (PG, difference between total protein and albumin) in the detection of hyper- or hypogammaglobulinemia is not well established. We assessed the performance of PG for the detection of hyper- or hypogammaglobulinemia in a large sample of patients. METHODS: We reviewed all paired measurements of serum total protein, albumin, quantitative immunoglobulins, and serum protein electrophoresis tested between March 2014 and June 2017 at the Eastern Ontario Regional Laboratory Association. Sensitivity, specificity, positive predictive value, negative predictive value and likelihood ratios of PG at thresholds between 18 and 44 g/L for the detection of hyper- and hypogammaglobulinemia were assessed. RESULTS: There were 19,575 and 5,426 simultaneous paired data points to assess hyper- and hypogammaglobulinemia identified by serum protein electrophoresis (SPE) and nephelometry, respectively. The mean PG was 36.3 g/L (SD 8.6). The prevalence of hypergammaglobulinemia (>16 g/L by SPE) and hypogammaglobulinemia (IgG <7 g/L) was 21.9 and 5.5%, respectively. High PG (≥38 g/L) had sensitivity and specificity of 76.2 and 71.5% respectively for hypergammaglobulinemia. PG ≥38 g/L had a negative predictive value (NPV) of 93.1% for monoclonal, and 96.9% for polyclonal gammopathy. A PG threshold of ≤18 g/L had of sensitivity of 0.4%, specificity of 100%, PPV of 100% and NPV of 80.1% to detect hypogammaglobulinemia (IgG <7 g/L). CONCLUSIONS: High and low PG values were not sensitive in detecting hyper- or hypogammaglobulinemia, although negative predictive values were high for both. Performance of PG should be further evaluated prospectively in specific populations at risk of for abnormal IgG levels.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".