Field evaluation in Chad of community usage of CD4 T lymphocyte counting by alternative single-platform flow cytometry
Bibliographic record
Abstract
BACKGROUND: Field and community evaluation of the routine usage of CD4 T counting platforms is essential in resource-poor countries for efficient and cost-effective monitoring of HIV-infected adults and children attending health care centers. METHODS: We herein addressed the principal issues raised by the implementation of the single-platform, volumetric Auto40 flow cytometer (Apogee Flow Systems Ltd, Hemel Hempstead, UK) in 8 community HIV monitoring laboratories of different levels throughout Chad. This is a country with particularly difficult conditions, both in terms of climate and vast geographical territory, making the decentralization of the therapeutic management of HIV-infected patients challenging. RESULTS: The routine usage of the Auto40 flow cytometers for a period of 5 years (2008-2013) confirms the reliability and robustness of the analyzer for community-based CD4 T cell enumeration in terms of both absolute numbers and percentages to enable accurate monitoring of HIV-infected adults and children. However, our observations suggest that the Auto40 mini flow cytometer is not suitable for all laboratories as it is oversized and ultimately very expensive. CONCLUSION: The Chad experience with the Auto40 flow cytometer suggests that its usage in resource-limited settings should be mainly reserved to reference (level 1) or district (level 2) laboratories, rather than to laboratories of health care centres (level 3).
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.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".