Bibliographic record
Abstract
<div> <i>April 28, 2026</i> </div> <div> <i>Forthcoming in Operations Research</i> </div> <div> <br> </div>Traditional queuing theory assumes that job types are perfectly observed and assigns each job to a type-specific priority queue—an approach we term type-driven priority queuing. We study feature-driven priority queuing, where types are unobserved and must be inferred from observable features using a classifier. We examine two implementations. The first, type-first, predicts type probabilities from features and then maps these probabilities to priority queues. The second, direct, bypasses type prediction and maps features directly to priority queues in an end-to-end manner. The classifiers in both implementations can be trained using labeled data of features (e.g., chest X-rays) with types (e.g., disease findings), but we train the direct classifier to minimize empirical waiting cost rather than type-prediction error.&nbsp;<span>In the type-first approach, the type classifier is optimized and locked in the first stage; queue assignment is then optimized to minimize an estimated waiting cost computed from the type classifier’s output distribution. The actual waiting cost, however, depends on the underlying feature distributions. The estimated waiting cost converges to the actual waiting cost only when the classifier recovers the Bayes posterior—a condition rarely satisfied by complex, misspecified models in high-dimensional settings. The direct approach instead optimizes an empirical waiting cost computed directly from the type-labeled features, ensuring convergence to the actual waiting cost and yielding systematically better queue assignments. We prove this advantage analytically, demonstrate it in tractable examples, and confirm it in large-scale simulations. In experiments with 100,000 chest X-rays and state-of-the-art deep learning classifiers, the direct approach can substantially reduce average radiologist waiting cost, driven by its ability to jointly capture delay-cost differences and limited feature separability when assigning priorities.</span>
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".