Evidence map›Paper›PMID 30488594›Full record

ArticlePhysiological reports2018

Moving beyond threshold-based dichotomous classification to improve the accuracy in classifying non-responders.

Jacob T Bonafiglia, Matthew W Nelms, Nicholas Preobrazenski, Camille LeBlanc, Lauren Robins, Simo Lu, Alexander Lithopoulos, Jeremy J Walsh, Brendon J Gurd

Abstract read
In one paragraph

Article in Physiological reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–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

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

9 authors.

Jacob T BonafigliaSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Matthew W NelmsSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Nicholas PreobrazenskiSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Camille LeBlancSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Lauren RobinsSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Simo LuSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Alexander LithopoulosSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.
Jeremy J WalshHealthy Active Living and Obesity Research Group, Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.
Brendon J GurdSchool of Kinesiology and Health Studies, Queen's University, Kingston, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We examined maximal oxygen consumption responses following exercise training to demonstrate the limitations associated with threshold-based dichotomous classification of responders and non-responders and proposed alternative methods for classification. Specifically, we: 1) calculated individual probabilities of response, and 2) classified individuals using response confidence intervals (CI) and reference points of zero and a smallest worthwhile change of 0.5 METs. Our findings support the use of individual probabilities and individual CIs to improve the accuracy in non-response classification.

Indexed as

Oxygen ConsumptionAdultBiological Variation, PopulationConfidence IntervalsData Interpretation, StatisticalHumansMalePhysical Conditioning, HumanIndividual responsesinterindividual variabilitynon-respondertypical errorVO2max

Identifiers

PMID30488594
PMCPMC6429972

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.