Cytofluorometric methods for assessing absolute numbers of cell subsets in blood
Bibliographic record
Abstract
The enumeration of absolute levels of cells and their subsets in clinical samples is of primary importance in human immunodeficiency virus (HIV)+ individuals (CD4+ T- lymphocyte enumeration), in patients who are candidates for autotransplantation (CD34+ hematopoietic progenitor cells), and in evaluating leukoreduced blood products (residual white blood cells). These measurements share a number of technical options, namely, single- or multiple-color cell staining and logical gating strategies. These can be accomplished using single- or dual-platform counting technologies employing cytometric methods. Dual-platform counting technologies couple the percentage of positive cell subsets obtained by cytometry and the absolute cell count obtained by automated hematology analyzers to derive the absolute value of such subsets. Despite having many conceptual and technical limitations, this approach is traditionally considered as the reference method for absolute cell count enumeration. As a result, the development of single-platform technologies has recently attracted attention with several different technical approaches now being readily available. These single-platform approaches have less sources of variability. A number of reports clearly demonstrate that they provide better coefficients of variation (CVs) in multicenter studies and a lower chance to generate aberrant results. These methods are therefore candidates for the new gold standard for absolute cell assessments. The currently available technical options are discussed in this review together with the results of some cross-comparative studies. Each analytical system has its own specific requirements as far as the dispensing precision steps are concerned. The importance of precision reverse pipetting is emphasized. Issues still under development include the establishment of the critical error ranges, which are different in each test setting, and the applicability of simplified low-cost techniques to be used in countries with limited resources.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".