BIOIMPEDANCE MARKERS AND TUBERCULOSIS OUTCOME AMONG HIV-INFECTED PATIENTS
Bibliographic record
Abstract
BACKGROUND: The changes in body composition markers (weight, fat mass, lean mass, and BMI) over time can be associated with TB treatment outcome among HIV-infected patients. The aim of this study was to investigate whether changes in fat mass and lean mass were associated with the treatment response among patients with HIV infection and pulmonary tuberculosis. MATERIALS AND METHODS: This was a prospective cohort study. Data from HIV-infected patients commencing TB therapy were analyzed. This included body weight measurement using bioimpedance equipment at baseline, one month, and two months after starting TB treatment. RESULTS: The study was conducted in 125 patients, 17 patients (13.6%) died during treatment, of which 5 died during the first month of treatment, 4 during the second month and 8 after the second month. The group of patients with good response, increased their weight by 1.3 kg (p <0.001) at the end of the first month of TB treatment and 2.6 kg in the second month (p <0.001), and body fat increase was 1.2 Kg (p <0.001) and 2.3 kg (p <0.001), the first and second month respectively. The group of patients who died had lost 2.1 kg fat mass after the first month (p <0.001) and 3.7 kg in the second month (p <0.001). CONCLUSIONS: Our results show that the weight change during TB treatment (increased fat mass) helps us predict therapeutic response. Weight loss during the first month of starting therapy should be evaluated thoroughly to identify the probable cause of treatment failure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".