Evidence map›Paper›PMID 35199065›Full record

ArticlePatterns (New York, N.Y.)2022

Ranks underlie outcome of combining classifiers: Quantitative roles for

Matthew J Sniatynski, John A Shepherd, Thomas Ernst, Lynne R Wilkens, D Frank Hsu, Bruce S Kristal

Abstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
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

6 authors.

Matthew J SniatynskiDivision of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, LM322B, Boston, MA 02115, USA.
John A ShepherdSchool of Medicine, University of California San Francisco, San Francisco, CA 94143, USA.
Thomas ErnstJohn A. Burns School of Medicine, University of Hawaii at Mānoa, Honolulu, HI 96813, USA.
Lynne R WilkensUniversity of Hawaii Cancer Center, University of Hawaii at Mānoa, Honolulu, HI 96813, USA.
D Frank HsuDepartment of Computer and Information Science, Fordham University, LL813, 113 West 60th Street, New York, NY 10023, USA.
Bruce S KristalDivision of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, LM322B, Boston, MA 02115, USA.

Funding

University of Hawaii Cancer Center CCSGP30CA071789 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI Xin Chen · 1996 to 2026
$56.2M
Recruitment and Data CollectionP01CA168530 · NCI · UNIVERSITY OF HAWAII AT MANOA · PI LE MARCHAND, LOIC · 2012 to 2016
$19.9M
Circadian Lipidomics in Constant Routine, Forced Desynchrony, and Non-lab SettingR01HL132556 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI KRISTAL, BRUCE S · 2016 to 2019
$3.5M
Lipidomics Biomarkers Link Sleep Restriction to Adiposity Phenotype, Diabetes, and Cardiovascular RiskR01HL140335 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI KRISTAL, BRUCE S · 2018 to 2021
$3.4M
Macronutrients, Mitochondria and Blood Metabolome/Proteome Disease Risk ProfilesU01ES016048 · NIEHS · BRIGHAM AND WOMEN'S HOSPITAL · PI KRISTAL, BRUCE S · 2007 to 2010
$1.9M
Serum Metabolomic/Proteomic Profiles in CALERIER01AG028996 · NIA · WINIFRED MASTERSON BURKE MED RES INST · PI KRISTAL, BRUCE S · 2006 to 2010
$1.8M
Validated Biomarkers for Primary Macronutrient BalanceRC1ES018411 · NIEHS · BRIGHAM AND WOMEN'S HOSPITAL · PI KRISTAL, BRUCE S · 2009 to 2010
$999k
High Resolution Plasma Lipidomics in CALERIER01AG045713 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI KRISTAL, BRUCE S · 2013 to 2014
$530k
NCI NIH HHS P01 CA168530NHLBI NIH HHS R01 HL132556NHLBI NIH HHS R01 HL140335NIA NIH HHS R01 AG028996NIA NIH HHS R01 AG045713NIEHS NIH HHS RC1 ES018411NIEHS NIH HHS U01 ES016048
6 · The paper itself

Abstract

Combining classifier systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Classification most commonly improves when the classifiers are "sufficiently good" (generalized as "

Indexed as

accuracycorrelationdata fusiondecision fusiondiversityinformation fusionmodel fusionranksscoressystem fusion

Identifiers

PMID35199065
PMCPMC8848007

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.