Rate of decline of absolute number and percentage of CD4 T lymphocytes among HIV-1-infected adults in Dar es Salaam, Tanzania
Bibliographic record
Abstract
OBJECTIVE: To determine the rate of decline of CD4 T lymphocytes among HIV-1-infected individuals. DESIGN AND SETTING: A prospective open cohort study of workers in three hotels in Dar es Salaam. METHODS: The workers were seen yearly during the study. CD4 T lymphocyte counts were determined using flow cytometry. The CD4 T-lymphocyte slopes were determined using a linear regression model. RESULTS: During the 9-year study period 682 subjects were selected for lymphocyte subset determinations. Of these, 94 HIV-1-seroprevalent (72%), 77 HIV-1-seroincident (67%) and 325 seronegative (75%) individuals had three or more CD4 T-cell determinations, and were used for calculations of CD4 cell slopes with a mean follow-up period of 71.4, 52.9 and 86.0 months, respectively. The median yearly decline of the CD4 T-lymphocyte counts and percentages among seroprevalent individuals was -21.5 cells/microl and -1.3%; among the seroincident individuals the median decline was -22.0 cells/microl and -1.5%. In seroincident individuals the mean duration to a CD4 T-lymphocyte level corresponding to a definition of AIDS was 13.3 years or 11.8 years for CD4 cell counts or percentages, respectively. HIV-1-seropositive subjects who died had significantly steeper CD4 cell slopes than those who survived. CONCLUSION: The rates of CD4 T-lymphocyte decline in HIV-1-infected individuals in our population are similar to those reported in Europe and north America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".