Improving Malignancy Detection Rates in Body Fluids Submitted to the Hematology Laboratory for Nucleated Cell Count and Differential: A Quality Improvement Study
Bibliographic record
Abstract
CONTEXT.—: Body fluid specimens are regularly submitted to the hematology laboratory for cell count and differential. Unless there is high clinical suspicion for malignancy, most cases lack concurrent cytology review and may not benefit from more focused examination for malignancy. OBJECTIVE.—: To compare rates of malignancy detection before and after fluid-focused training for hematology technologists as part of a quality improvement initiative. DESIGN.—: During an 8-week pretraining period, body fluids submitted to the cytology laboratory were correlated with concurrent hematology specimens. After slide review and training sessions for the hematology technologists, the same data were collected for a 4-week period. Discrepant cases were reviewed by hematology laboratory supervisors and pathologists. RESULTS.—: We collected 465 pretraining and 249 posttraining body fluids with concurrent cytology and hematology evaluation. In the pretraining cohort, 48 cases (10.3%) were diagnosed as malignant by cytology; of those, 33 were detected by hematology. In the posttraining cohort, 30 cases (12.0%) were diagnosed as malignant by cytology of which 27 were detected by hematology. Of the 18 discrepant cases (all carcinomas), hematology slide review showed definite features of malignancy in 15 and no tumor cells in 3. The malignancy detection rate by the hematology laboratory significantly improved after training (68.8% versus 90.0%, P = .01). CONCLUSIONS.—: We demonstrate the comparatively lower malignancy detection rate for body fluid specimens processed in our hematology laboratory, particularly for carcinomas. Hematology technologist education/training improved the malignancy detection rate, an important quality improvement given the large proportion of body fluids undergoing hematology evaluation without concurrent cytology reviews.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".