Standardization of haematology critical results management in adults: an International Council for Standardization in Haematology, ICSH, survey and recommendations
Bibliographic record
Abstract
INTRODUCTION: These recommendations are intended to develop a consensus in the previously published papers as to which parameters and what values should be considered critical. A practical guide on the standardization of critical results management in haematology laboratories would be beneficial as part of good laboratory and clinical practice and for use by laboratory-accrediting agencies. METHODS: A working group with members from Europe, America, Australasia and Asia was formed by International Council for Standardization in Haematology. A pattern of practice survey of 21 questions was distributed in 2014, and the data were collected electronically by Survey Monkey. The mode, or most commonly occurring value, was selected as the threshold for the upper and lower alert limits for critical results reporting. RESULTS: A total of 666 laboratories submitted data to this study and, of these, 499 submitted complete responses. Full blood count critical results alert thresholds, morphology findings that trigger critical result notification, critical results alert list, notification process and maintenance of critical results management protocol are described. This international survey provided a snapshot of the current practice worldwide and has identified the existence of considerable heterogeneity of critical results management. CONCLUSION: The recommendations in this study represent a consensus of good laboratory practice. They are intended to encourage the implementation of a standardized critical results management protocol in the laboratory.
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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 | 0.159 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".