ArticleObesity surgery2025
If the Only Tool is a Hammer, Every Problem Looks Like a Nail-Diversifying the Analytical Approaches of Data from Primary Research for Better Understanding of Effectiveness, Outcomes and Impacts of Metabolic and Bariatric Surgery.
Article in Obesity surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is a notable increase in metabolic and bariatric surgery (MBS) procedures, the numbers of patients treated, and volume of scientific articles published. However, it is not clear whether there is a parallel diversification in the analytic approaches employed in published MBS research. The current study appraised this point.
methodsUsing the Obesity Surgery journal's website, we undertook a rapid scoping review of random recently published original quantitative primary research. The approaches used for data analyses were noted and categorized.
resultsThree main approaches to data analyses were observed, but one approach was much more frequently used across the examined studies. Approach 1-used by most investigations, traditionally examined few or several individual risk factor/s and followed them up post-surgery, i.e., isolated indicators of effectiveness appraised cross-sectionally or longitudinally across timepoint/s, and compared to baseline values. Approach II-employed by much fewer studies, used cluster analysis, i.e., clusters of patients grouped together based on common characteristics or several risk factors considered concurrently (clinical quantitative traits), and appraised cross-sectionally or longitudinally in relation to outcomes. Approach III-again employed by much fewer studies, utilized "composite" indicators, i.e., several conjoined outcomes integrated together, or converted to risk, measured at mid or longer-term timepoint/s to represent impact.
conclusionsAnalytical approaches employed in quantitative primary bariatric research do not exhibit sufficient diversification. Some data analytical approaches can reach conceptual areas that other approaches cannot reach, to provide different clues to the evidence puzzle. Over-reliance on some analytic approaches can result in tackling MBS research problems in sub-optimal ways that are not always the most advantageous for the research questions being examined. Bariatric research teams need to be aware of the range of possible approaches, and to use the best tool/s subject to the problem being appraised and type of evidence sought. More analytic diversity of data of primary studies can enhance the developing MBS knowledge, provide different perspectives of the puzzle, and reduce "blind spots" in the MBS evidence base.
Indexed as
Identifiers
40815401What OpenQuestion holds
Registered trials
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.