The adaptation of AABACUS for quality improvement in laboratory workflow analysis ("L-AABACUS")
Bibliographic record
Abstract
The ability to effectively monitor key indicators is important for continuous quality improvement in laboratory immunohistochemistry. This article deals specifically with laboratory turnaround time (TAT) as a key delivery indicator and the impact of laboratory workflow on laboratory TATs. While our laboratory has traditionally relied on the manual calculation of slide-TAT (S-TAT) to monitor delivery, we have determined that automated calculation of case-TAT (C-TAT) would be superior as a delivery indicator. AABACUS (Automatable Activity-Based Approach to Complexity Unit Scoring) is an activity-based workload model designed to function primarily as a decision support tool to monitor pathologist staffing levels. We devised a high-level proof-of-principle approach to determine whether it is possible to apply AABACUS as a decision support tool for quality improvement through analysis of alternative laboratory workflows that have potential to impact C-TAT. Our use of AABACUS in this proof-of-principle quality improvement endeavour was two-fold: (1) we leveraged the ability of AABACUS to link data at the slide level to data at the case level, which enabled the automated calculation of C-TAT; and (2) we adapted AABACUS to evaluate the impact of laboratory workflow activities (specifically workflow bifurcation activities) on the calculated C-TATs. We have coined the term 'L-AABACUS' to describe the adaptation of AABACUS to the analysis of laboratory workflow.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".