ReviewJournal of clinical medicine2024
The Unappreciated Value of a Cheap, 'Good Enough' Method of Detecting Thyroid Cancer.
Review in Journal of clinical medicine, 2024. 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.
- Molecular Diagnostics and Personalized Therapeutics in Differentiated Thyroid Carcinoma: A Clinically Oriented Review.Diagnostics (Basel, Switzerland) · 2025Review
- Impact of geriatric syndrome burden on healthcare services utilization and mortality among community-dwelling older adults: is it still too late to do something?European geriatric medicine · 2025Observational
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
The advent of advanced molecular diagnostic techniques has revealed plenty of information about signaling pathways and gene regulation in cancer, as well as new inputs for the classification of cancer subtypes, diagnosis, prognosis, and prediction of response to therapy. However, in most cases we do not have single biomarkers yet and, therefore, the final diagnosis is often rendered by the combination of multiple results by means of complex algorithms, eventually leading to an increase in their costs. The problem of the costs of such tests is particularly relevant in the case of thyroid cancer (TC), because of the observed increase in the number of patients affected by thyroid nodules (TN)s, in what is considered a global pandemic. High-income countries can afford the cost of the advanced molecular tests for such a multitude of TNs, since they are covered by private insurances. People living in upper-middle, lower-middle, and especially in low-income countries, where the costs for these advanced molecular tests are supported by general taxation and out-of-pocket payments, are exposed as a personal financial burden. Immunohistochemistry in cancer management represents an extremely cost-effective method in different clinical scenarios. In the preoperative recognition of TC, the use of such method, based on Galectin-3 and others protein markers, such as HMBE1, proved to be effective in diagnosing TC in TNs indeterminate at conventional cytology (Bethesda classification III or IV), with an extremely low cost. Moreover, Galectin-3 fulfills one of the major criteria of an ideal marker, being involved in the thyroid cell transformation. Despite this evidence, Galectin-3 ThyroTest is not considered and not even mentioned in many reviews, focused on the appropriate identification of TC, as well as in studies where the cost-effectiveness of the different approaches is comparatively evaluated. The aim of this review is to emphasize the value of the Galectin-3 based immunohistochemistry as a cheap and "good enough" method in the preoperative diagnosis of TC especially in, but not limited to, low-middle income countries.
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.