Partial VOROS: A Cost-aware Performance Metric for Binary Classifiers with Precision and Capacity Constraints.

Christopher Ratigan, Kyle Heuton, Carissa Wang, Lenore Cowen, Michael C Hughes

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Christopher RatiganDepartment of Mathematics, Tufts University, Medford, MA, USA.

Kyle HeutonDepartment of Computer Science, Tufts University, Medford, MA, USA.

Carissa WangDepartment of Computer Science, Tufts University, Medford, MA, USA.

Lenore CowenDepartment of Mathematics, Tufts University, Medford, MA, USA.

Michael C HughesDepartment of Computer Science, Tufts University, Medford, MA, USA.

Funding

Focused Imaging as a Novel Diagnostic Strategy for Aortic StenosisR01HL180937 · NHLBI · TUFTS MEDICAL CENTER · PI Michael C. Hughes, Benjamin Seth Wessler · 2025 to 2026

$1.5M

NHLBI NIH HHS R01 HL180937

6 · The paper itself

Abstract

The ROC curve is widely used to assess binary classifiers. Yet for some applications, such as alert systems for monitoring hospitalized patients, conventional ROC analysis cannot meet two key deployment needs: enforcing a constraint on precision to avoid false alarm fatigue and imposing an upper bound on the number of predicted positives to represent the capacity of hospital staff. The usual area under the curve metric also does not reflect asymmetric costs for false positives and false negatives. In this paper we address all three of these issues. First, we show how the subset of classifiers that meet precision and capacity constraints occupy a feasible region in ROC space. We establish the polygonshaped geometry of this region. We then define the

Identifiers

PMID42756865

PMCPMC13583593

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.