A model to rule out smear-negative tuberculosis among symptomatic HIV patients using C-reactive protein
Bibliographic record
Abstract
SETTING: Improved diagnostic algorithms for sputum smear-negative tuberculosis (SNTB) are needed to address the dramatic increase in SNTB in regions with high human immunodeficiency virus (HIV) prevalence. OBJECTIVE: To determine whether the addition of C-reactive protein (CRP) to a prediction model using simple clinical criteria improves the diagnosis of SNTB among mostly antiretroviral-naïve adult HIV TB suspects in an out-patient setting. DESIGN: A multiple logistic regression model was derived from a database of 228 HIV patients to predict the risk of SNTB using data from a previous prospective study. RESULTS: The derived model demonstrated that male sex, night sweats, fever, low body mass index and anemia increased the probability of having SNTB. CRP improved the accuracy of the model (without CRP, area under the curve [AUC] 0.75, 95%CI 0.68-0.81 vs. model with CRP, AUC 0.81, 95%CI 0.76-0.87, P = 0.0014) to predict SNTB. Using reclassification tables, CRP correctly reclassified 27.9% of the patients (net reclassification improvement, P = 0.0005) into higher or lower risk categories. The strongest effect was seen in the reclassification improvement among patients with no TB, which was 20.6% (P = 0.0023). CONCLUSION: CRP improved the performance of the prediction model in the diagnosis of SNTB in HIV patients, and may play a role in ruling out SNTB in this population. Prospective validation of this model is needed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".