Achieving High Reliability in Histology
Bibliographic record
Abstract
OBJECTIVES: Despite sweeping medical advances in other fields, histology processes have by and large remained constant over the past 175 years. Patient label identification errors are a known liability in the laboratory and can be devastating, resulting in incorrect diagnoses and inappropriate treatment. The objective of this study was to identify vulnerable steps in the histology workflow and reduce the frequency of labeling errors (LEs). METHODS: In this 36-month study period, a numerical step key (SK) was developed to capture LEs. The two most prevalent root causes were targeted for Lean workflow redesign: manual slide printing and microtome cutting. The numbers and rates of LEs before and after interventions were compared to evaluate the effectiveness of interventions. RESULTS: Following the adoption of a barcode-enabled laboratory information system, the error rate decreased from a baseline of 1.03% (794 errors in 76,958 cases) to 0.28% (107 errors in 37,880 cases). After the implementation of an innovative ice tool box, allowing single-piece workflow for histology microtome cutting, the rate came down to 0.22% (119 errors in 54,342 cases). CONCLUSIONS: The study pointed out the importance of tracking and understanding LEs by using a simple numerical SK and quantified the effectiveness of two customized Lean interventions. Overall, a 78.64% reduction in LEs and a 35.28% reduction in time spent on rework have been observed since the study began.
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.
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".