SynthesisObesity (Silver Spring, Md.)2020
Construction and Use of Body Weight Measures from Administrative Data in a Large National Health System: A Systematic Review.
Synthesis in Obesity (Silver Spring, Md.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Is CONSORT a Risk of Bias Tool for Experimental Studies: A Big Misunderstanding.Journal of caring sciences · 2025Article
- Deriving Weight From Big Data: Comparison of Body Weight Measurement-Cleaning Algorithms.JMIR medical informatics · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveAdministrative data are increasingly used in research and evaluation yet lack standardized guidelines for constructing measures using these data. Body weight measures from administrative data serve critical functions of monitoring patient health, evaluating interventions, and informing research. This study aimed to describe the algorithms used by researchers to construct and use weight measures.
methodsA structured, systematic literature review of studies that constructed body weight measures from the Veterans Health Administration was conducted. Key information regarding time frames and time windows of data collection, measure calculations, data cleaning, treatment of missing and outlier weight values, and validation processes was collected.
resultsWe identified 39 studies out of 492 nonduplicated records for inclusion. Studies parameterized weight outcomes as change in weight from baseline to follow-up (62%), weight trajectory over time (21%), proportion of participants meeting weight threshold (46%), or multiple methods (28%). Most (90%) reported total time in follow-up and number of time points. Fewer reported time windows (54%), outlier values (51%), missing values (34%), or validation strategies (15%).
conclusionsA high variability in the operationalization of weight measures was found. Improving methods to construct clinical measures will support transparency and replicability in approaches, guide interpretation of findings, and facilitate comparisons across studies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.