Critical values in Hematology
Bibliographic record
Abstract
INTRODUCTION: Critical values are life-threatening results that require immediate notification to the patient's healthcare provider. Accreditation bodies require laboratories to establish critical values. A survey of Ontario laboratories was conducted to determine current practice for critical values in hematology. METHODS: The survey was sent to 182 participants questioning sources for establishing critical values, levels, review frequency, delta checks, and reporting. The survey was completed by laboratory managers, supervisors, technical specialists, senior technologists, and bench technologists working in hematology. RESULTS: The majority of participating laboratories have established critical values limits for hemoglobin, leukocyte counts, and platelet counts. Most laboratories also include the presence of malaria parasites and blast cells. Some laboratories reported the presence of plasma cells, sickle cells, schistocytes, and spherocytes as critical values. Multiple sources are used for establishing a critical value policy. There was variability for the frequency of critical values review. Rules may differ for a first-time patient sample vs. a repeat patient sample. Delta checks are seldom used to determine whether a result should be called a critical value. Most participants require the individual taking the critical result(s) to read back and confirm that they are directly involved with the patient's care. CONCLUSION: There is a lack of consensus for critical values reporting in hematology. As critical value reporting is crucial for patient safety, standardization of this practice would be beneficial.
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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 | 0.031 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".