ArticleBMC public health2025
A novel model for accurate and fast prediction of cancer incidence.
Article in BMC public health, 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
backgroundPredicting cancer incidence has long been a challenge for clinicians and researchers. Accurate predictions are essential for health planning to ensure adequate resources for diagnosis, treatment, and rehabilitation. Current prediction methods rely on historical data, assuming persistent patterns of cancer incidence.
methodIn this study, the Google Trends tool was used to obtain the relative search volume index (RSVI) for the topic "cancer" each year from 2017 to 2023 in the United States and worldwide. The proposed model incorporated actual cancer incidence rates and yearly changes in RSVI.
resultsThe model was applied to predict the rates of new cancer cases in fifty American states over four consecutive years (2017, 2018, 2019, 2020). The selection of years was restricted with data availability. In most states, the percentage error did not exceed 6%. The high degree of similarity between the actual and predicted cancer incidence rates was notable. Similar results were obtained when predicting cancer incidence rates in the countries studied.
conclusionThe model has successfully provided accurate short-term predictions of cancer incidence rates across all 50 American states and 54 countries since 2017.
Indexed as
Identifiers
What 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.