ISHAGE protocol: Are we doing it correctly?
Bibliographic record
Abstract
BACKGROUND: Flow cytometric CD34(+) stem cell enumeration is routinely performed to optimize timing of peripheral blood stem cell collections and assess engraftment capability of the apheresis product. While a number of different flow methodologies have been described, the highly standardized ISHAGE protocol is currently the most widely employed, with 204/255 (81%) international participants in the UK NEQAS CD34(+) stem cell enumeration program indicating their use of this method. Recently, two laboratories were identified as persistent poor performers, a fact attributed to incorrect ISHAGE protocol usage/setup. This prompted UK NEQAS to question whether other laboratories were making similar errors and, if so, how this might affect individual EQA performance. METHODS AND RESULTS: In send out 0801, where two stabilized samples were issued, the EQA center surveyed 255 participants with flow analysis data and subsequent results collected. One hundred and ninety-six laboratories returned results with 103 returning dot plots. Eighty-three out of one hundred and three stated that they used the ISHAGE protocol gating strategy but 43% (36/83) were incorrectly set-up. Analysis of the data showed those incorrectly using single platform ISHAGE gating strategy were twice as likely to fail an EQA exercise compared to those using the protocol correctly. This failure rate increased two fold when incorrect ISHAGE protocol was used in a dual platform setting. CONCLUSION: This study suggests a widespread fundamental lack of understanding of the ISHAGE protocol and the need to deploy it correctly, potentially having significant clinical implications and highlights the need to monitor participants rigorously in their deployment of the ISHAGE protocol. It is hoped that once these findings have been disseminated, performance can be improved.
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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 | 0.202 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.012 |
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".