Digital clubbing in tuberculosis – relationship to HIV infection, extent of disease and hypoalbuminemia
Bibliographic record
Abstract
BACKGROUND: Digital clubbing is a sign of chest disease known since the time of Hippocrates. Its association with tuberculosis (TB) has not been well studied, particularly in Africa where TB is common. The prevalence of clubbing in patients with pulmonary TB and its association with Human Immunodeficiency Virus (HIV), severity of disease, and nutritional status was assessed. METHODS: A cross-sectional study was carried out among patients with smear-positive TB recruited consecutively from the medical and TB wards and outpatient clinics at a public hospital in Uganda. The presence of clubbing was assessed by clinical signs and measurement of the ratio of the distal and inter-phalangeal diameters (DPD/IPD) of both index fingers. Clubbing was defined as a ratio > 1.0. Chest radiograph, serum albumin and HIV testing were done. RESULTS: Two hundred patients (82% HIV-infected) participated; 34% had clubbing by clinical criteria whilst 30% had clubbing based on DPD/IPD ratio. Smear grade, extensive or cavitary disease, early versus late HIV disease, and hypoalbuminemia were not associated with clubbing. Clubbing was more common among patients with a lower Karnofsky performance scale score or with prior TB. CONCLUSION: Clubbing occurs in up to one-third of Ugandan patients with pulmonary TB. Clubbing was not associated with stage of HIV infection, extensive disease or hypoalbuminemia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".