T‐cell subset counting and the fight against AIDS: Reflections over a 20‐year struggle
Bibliographic record
Abstract
The story of T-lymphocyte subset immunophenotyping technology is reviewed on the occasion of the 20th anniversary of CD4 T-cell enumeration. Over time, immunophenotyping has evolved into precise, reliable, but complicated and expensive technology requiring fresh blood samples. The gating technologies that were universally adapted for clinical flow cytometry for the past decade relied on rapidly deteriorating morphological scatter characteristics of leukocytes. This special issue dedicated to CD4 T-cell enumeration features most of the available new options that will have a significant impact on how this technology will be implemented within the first decade of the 21st century. In a series of original publications, including the new NIH guideline for T-cell subset enumeration, contemporary gating protocols that use immunologically logical parameters are presented as part of the more reliable and affordable immunophenotyping alternative. Some of the improvements addressed here include the costs of the assays and the capacity to monitor interlaboratory and intralaboratory performances. It is clear that an effective attack on the human immunodeficiency virus (HIV) epidemic has to embrace resource-poor regions. Reducing the cost of the assay while improving reliability and durability is a move in the right direction.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".