Point-of-care CD4 devices for staging and monitoring of HIV infected individuals: what is the evidence?
Bibliographic record
Abstract
Background: At the end of 2012, worldwide, approximately 35.5 million people were living with HIV. 1 HIV mortality rates, in resource limited settings are now starting to improve as antiretroviral therapy (ART) is becoming universally available.Now, these settings need to improve the care provided to patients.Good quality care requires timely detection, staging and initiation of therapy.CD4+ cell counting, point-of-care (POC) devices could improve the quality of care in resource limited settings, by allowing for the decentralization of HIV care.As a result, these POC CD4+ cells assays could circumvent patient barriers to care, and relieve building pressure on regional laboratories.Several POC CD4+ devices are available, but an independent comparison of performance has not yet been done.The aim of this thesis is to evaluate the current evidence for POC CD4+ cell counting technologies and determine whether their performance would allow them to be used interchangeably with the current gold standard. Methods:To attain our objective we completed a systematic review and meta-analysis of the evidence relating to POC CD4+ cell assays.Our populations of interest were global populations of adults with a HIV+ status.Our outcome of interest was to the absolute Bland Altman mean bias, which represents the agreement between the POC device and the gold standard.We systematically searched 19 databases, relevant conferences and grey literature for the period 2000 to 2013.Of 4154 citations found, 16 articles were selected.A Bayesian hierarchical normal-normal model was used to meta-analyze data.Findings: POC devices appear to perform best in capillary samples.Only sufficient data was available to allow for a meta-analysis of the PIMA device; a smaller BA mean bias in capillary blood vs. venous specimens was found (-3.0 cells/L; 95% CrI: -282 to 228 vs. -265 cells/L;
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".