Evidence map›Paper›PMID 39883837›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Errors are robustly tamed in cumulative knowledge processes.

Anna Brandenberger, Cassandra Marcussen, Elchanan Mossel, Madhu Sudan

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Anna BrandenbergerDepartment of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0002-3781-0846
Cassandra MarcussenComputer Science, School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134.ORCID 0009-0009-1335-6640
Elchanan MosselDepartment of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139.ORCID 0000-0001-7812-7886
Madhu SudanComputer Science, School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134.ORCID 0000-0003-3718-6489

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As knowledge accumulates in science and society in a distributed fashion, erroneous derivations can be introduced into the corpus of knowledge. Such derivations can compromise the validity of any units of knowledge that rely on them in the future. Can societal knowledge maintain some level of integrity given simple distributed error-checking mechanisms? In this paper, we investigate the following formulation of the question: assuming that a constant fraction of the new derivations is wrong, is it possible for simple error-checking mechanisms that apply when a new unit of knowledge is derived to maintain the integrity of the corpus of knowledge? This question was introduced by Ben-Eliezer et al. ["Is this correct? Let's check!"

Indexed as

error eliminationknowledge accumulationlocal algorithmsprobabilistic models

Identifiers

PMID39883837
PMCPMC11804617

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.