Evidence map›Paper›PMID 28321295›Full record

ArticleArchives of public health = Archives belges de sante publique2017

Getting started with tables.

Hazel Inskip, Georgia Ntani, Leo Westbury, Chiara Di Gravio, Stefania D'Angelo, Camille Parsons, Janis Baird

Abstract read
In one paragraph

Article in Archives of public health = Archives belges de sante publique, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Hazel InskipMRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK.ORCID 0000-0001-8897-1749
Georgia NtaniMRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK.
Leo WestburyMRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK.
Chiara Di GravioMRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK.
Stefania D'AngeloMRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK.
Camille ParsonsMRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK.
Janis BairdMRC Lifecourse Epidemiology Unit, University of Southampton, Southampton General Hospital, Southampton, SO16 6YD UK.

Funding

Medical Research Council MC_UU_12011/4
6 · The paper itself

Abstract

backgroundTables are often overlooked by many readers of papers who tend to focus on the text. Good tables tell much of the story of a paper and give a richer insight into the details of the study participants and the main research findings. Being confident in reading tables and constructing clear tables are important skills for researchers to master.

methodCommon forms of tables were considered, along with the standard statistics used in them. Papers in the Archives of Public Health published during 2015 and 2016 were hand-searched for examples to illustrate the points being made. Presentation of graphs and figures were not considered as they are outside the scope of the paper.

resultsBasic statistical concepts are outlined to aid understanding of each of the tables presented. The first table in many papers gives an overview of the study population and its characteristics, usually giving numbers and percentages of the study population in different categories (e.g. by sex, educational attainment, smoking status) and summaries of measured characteristics (continuous variables) of the participants (e.g. age, height, body mass index). Tables giving the results of the analyses follow; these often include summaries of characteristics in different groups of participants, as well as relationships between the outcome under study and the exposure of interest. For continuous outcome data, results are often expressed as differences between means, or regression or correlation coefficients. Ratio/relative measures (e.g. relative risks, odds ratios) are usually used for binary outcome measures that take one of two values for each study participants (e.g. dead versus alive, obese versus non-obese). Tables come in many forms, but various standard types are described here.

conclusionClear tables provide much of the important detail in a paper and researchers are encouraged to read and construct them with care.

Indexed as

CategoriesCharacteristicsCorrelation coefficientsInter-quartile rangeMeanMedianRatiosRegression coefficientsRelative measuresStandard deviationTablesVariables

Identifiers

PMID28321295
PMCPMC5357815

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.