ArticleAmerican journal of clinical oncology2025
Logistic Model in Clinical and Health Research-the Elephant and Blind Men.
Article in American journal of clinical oncology, 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
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Logistic models are everywhere in scientific research, including clinical and health research. However, there is no practical report on research results that will utilize many available statistical tools but mostly 1 or 2 only, for a logistic model. We introduce 6 powerful statistical tools, forest plot, AUC curve, nomogram, and decision curve analysis, as well as bootstrapping sampling and cross validation, by applying head and neck cancer data to a logistic model. We hope that these tools will make the logistic model, and accordingly our research, more comprehensive and clinically more applicable. In the 6 parts of the article, we introduce each of the 6 statistical tools (methods) with relevant figures and interpretations on key statistical concepts to show how we can improve our understanding on the logistic model and the clinical prospects behind the data. The statistical tools we present in the current special communication for reporting research or clinical trials in a logistic model, if popularized among researchers and clinicians, will make a research conclusion more comprehensive, valid and clinically applicable to other cases.
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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.