Safe Reduction in CD4 Cell Count Monitoring in Stable, Virally Suppressed Patients With HIV Infection or HIV/Hepatitis C Virus Coinfection
Bibliographic record
Abstract
BACKGROUND: It has been suggested that routine CD4 cell count monitoring in human immunodeficiency virus (HIV)-monoinfected patients with suppressed viral loads and CD4 cell counts >300 cell/μL could be reduced to annual. HIV/hepatitis C virus (HCV) coinfection is frequent, but evidence supporting similar reductions in CD4 cell count monitoring is lacking for this population. We determined whether CD4 cell count monitoring could be reduced in monoinfected and coinfected patients by estimating the probability of maintaining CD4 cell counts ≥200 cells/µL during continuous HIV suppression. METHODS: The PISCIS Cohort study included data from 14 539 patients aged ≥16 years from 10 hospitals in Catalonia and 2 in the Balearic Islands (Spain) since January 1998. All patients who had at least one period of 6 months of continuous HIV suppression were included in this analysis. Cumulative probabilities with 95% confidence intervals were calculated using the Kaplan-Meier estimator stratified by the initial CD4 cell count at the period of continuous suppression initiation. RESULTS: A total of 8695 patients were included. CD4 cell counts fell to <200 cells/µL in 7.4% patients, and the proportion was lower in patients with an initial count >350 cells/µL (1.8%) and higher in those with an initial count of 200-249 cells/µL (23.1%). CD4 cell counts fell to <200 cells/µL in 5.7% of monoinfected and 11.1% of coinfected patients. Of monoinfected patients with an initial CD4 cell count of 300-349 cells/µL, 95.6% maintained counts ≥200 cells/µL. In the coinfected group with the same initial count, this rate was lower, but 97.6% of coinfected patients with initial counts >350 cells/µL maintained counts ≥200 cells/µL. CONCLUSIONS: From our data, it can be inferred that CD4 cell count monitoring can be safely performed annually in HIV-monoinfected patients with CD4 cell counts >300 cells/µL and HIV/HCV-coinfected patients with counts >350 cells/µL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".