Affordable CD4 T-cell enumeration for resource-limited regions: A status report for 2008
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The global struggle with human immunodeficiency virus (HIV) and the battle to develop affordable CD4 T-cell counting technology are both unfulfilled goals in 2008. The need for such instrumentation is more critical now as implementation of antiretroviral therapy (ART) is in progress in many resource limited regions. Major scaling-up efforts in rural situations are difficult to implement without laboratory infrastructure. CD4 T-cell counting is especially critical when trying to reach individuals with HIV to have them enrolled in ART as soon as they qualify for treatment based on CD4 count. METHOD: This review covers both the chronological evolution and the scientific milestones of technological development of affordable immunophenotyping. It is more focused on flow cytometry but does consider the potential contribution by digital image cytometry. RESULTS: Thus far flow cytometry offered only modest progress toward affordable immunophenotyping. A list with desirable features is offered for side by side comparison. Digital image cytometry has yet to show its enormous affordable market potential. CONCLUSIONS: It is possible to develop truly affordable, portable flow cytometry but it is not here yet. There are some hopeful signs as there are innovative and practical technical components appearing at regular intervals. However, so far the technical breakthroughs have been fragmented efforts without any attempts to consider intercorporate collaboration to optimize critical mass and synergy. The smaller players in the industry have made some progress toward meeting the monumental needs in Africa and Asia. Digital image cytometry may well be the ultimate winner in the affordable technology race.
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.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it