Common flow cytometry pitfalls in diagnostic hematopathology
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
Flow cytometry (FC) has proven to be an extremely versatile and useful tool in the diagnosis and monitoring of hematological diseases in addition to numerous other applications. Major advances in electronics, software, and reagents over the past years have simplified some aspects of FC, while at the same time the ability to combine 8-10 antibodies in a single tube can create both technical and interpretation issues that are more difficult to detect when using only 3-4 color combinations. Use of multiparameter panels can facilitate identification of abnormal populations; however, characteristics of the neoplastic population may create potential diagnostic pitfalls. An understanding of normal immunophenotypic patterns in states of rest, recovery, and activation is a critical first step in order to appropriately identify the abnormal populations that characterize hematopoietic neoplasms. Additionally, incorporation of newer therapeutic strategies, in particular targeted therapies, can confound standard methods for flow cytometric data analysis and knowledge of the impact of therapy on flow cytometric data is critical for accurate data interpretation. This manuscript will review preanalytical, instrument, and interpretation issues that may lead to incorrect interpretation of results.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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