Head to head comparisons in performance of CD4 point-of-care assays: a Bayesian meta-analysis (2000–2013)
Bibliographic record
Abstract
Abstract Timely detection, staging, and treatment initiation are pertinent to controlling HIV infection. CD4+ cell-based point-of-care (POC) devices offer the potential to rapidly stage patients, and decide on initiating treatment, but a comparative evaluation of their performance has not yet been performed. With this in mind, we conducted a systematic review and meta-analyses. For the period January 2000 to April 2014, 19 databases were systematically searched, 6619 citations retrieved, and 25 articles selected. Diagnostic performance was compared across devices (i.e., PIMA, CyFlow, miniPOC, MBioCD4 System) and across specimens (i.e., capillary blood vs. venous blood). A Bayesian approach was used to meta-analyze the data. The primary outcome, the Bland–Altman (BA) mean bias (which represents agreement between cell counts from POC device and flow cytometry), was analyzed with a Bayesian hierarchical normal model. We performed a head-to-head comparison of two POC devices such as PIMA and PointCareNOW CD4. PIMA appears to perform better vs. PointCareNOW with venous samples (BA mean bias: –9.5 cells/μL; 95% CrI: –37.71 to 18.27, vs. 139.3 cells/μL; 95% CrI: –0.85 to 267.4, mean difference = 148.8, 95% CrI: 11.8, 285.8); however, PIMA's best performed when used with capillary samples (BA mean bias: 2.2 cells/μL; 95% CrI: –19.32 to 23.6). Sufficient data were available to allow pooling of sensitivity and specificity data only at the 350 cells/μL cutoff. For PIMA device sensitivity 91.6 (84.7–95.5) and specificity was 94.8 (90.1–97.3), respectively. There were not sufficient data to allow comparisons between any other devices. PIMA device was comparable to flow cytometry. The estimated differences between the CD4+ cell counts of the device and the reference was small and best estimated in capillary blood specimens. As the evidence stands, the PointCareNOW device will need to improve prior to widespread use and more data on MBio and MiniPOC are needed. Findings inform implementation of PIMA and improvements in other CD4 POC device prior to recommending widespread use.
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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 | 0.068 | 0.129 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.071 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".