Evidence map›Paper›PMID 37477148›Full record

ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2023

Perspective: A resident's role in promoting safe machine-learning tools in sleep medicine.

Colin M Smith, Martina Vendrame

Abstract read
In one paragraph

Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2023. 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

2 authors.

Colin M SmithLehigh Valley Fleming Neuroscience Institute, Lehigh Valley Health Network, Allentown, Pennsylvania.
Martina VendrameLehigh Valley Fleming Neuroscience Institute, Lehigh Valley Health Network, Allentown, Pennsylvania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Residents and fellows can play a helpful role in promoting safe and effective machine-learning tools in sleep medicine. Here we highlight the importance of establishing ground truths, considering key variables, and prioritizing transparency and accountability in the development of machine-learning tools within the field of artificial intelligence. Through understanding, communication, and collaboration, in-training physicians have a meaningful opportunity to help progress the field toward safe machine-learning tools in sleep medicine. CITATION: Smith CM, Vendrame M. Perspective: a resident's role in promoting safe machine-learning tools in sleep medicine.

Indexed as

Internship and ResidencyPhysiciansArtificial IntelligenceHumansMachine LearningSleepartificial intelligence (AI)ground truthsmachine learningpolysomnography

Identifiers

PMID37477148
PMCPMC10620660

What OpenQuestion holds

Textmetadata
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.