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 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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".