Evidence map›Paper›PMID 37760625›Full record

ArticleCancers2023

Dynamic Risk Stratification Integrated with ATA Risk System for Predicting Long-Term Outcome in Papillary Thyroid Cancer.

Laura Valerio, Cristina Dalmiglio, Fabio Maino, Elisa Mattii, Andrea Trimarchi, Alessandra Cartocci, Maria Grazia Castagna

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 24% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 4 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Laura ValerioDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.ORCID 0000-0001-7723-7020
Cristina DalmiglioDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.
Fabio MainoDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.
Elisa MattiiDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.
Andrea TrimarchiDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.ORCID 0009-0000-4247-0869
Alessandra CartocciDepartment of Medical Biotechnologies, University of Siena, 53100 Siena, Italy.
Maria Grazia CastagnaDepartment of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.
University of Siena · IT

Funding

This research was funded by PRIN project #20205HZBP8 #20205HZBP8
6 · The paper itself

Abstract

backgroundIn recent years, there has been a renewed interest in thyroid cancer management paradigms that use individualized risk assessments as the basis for treatment and follow-up recommendations. In this study, we assumed that the long-term follow-up of differentiated thyroid cancer patients might be better tailored by integrating the response to initial therapy with the America Thyroid Association (ATA) risk classes.

methodsThis retrospective study included low- and intermediate-risk papillary thyroid cancer (PTC) patients followed up for a median time of 8 years and classified according to the response to initial therapy assessed 6-12 months after initial treatment.

resultsAfter a median follow-up of 8 years, in the initial excellent response subgroup of PTC patients (

conclusionsThis study of a large cohort of PTC patients showed that the initial ATA risk criteria may be useful for improving the risk-adapted management of PTC patients based on the response to initial therapy.

Indexed as

dynamic risk stratificationoutcomepostsurgical risk stratificationthyroid cancer

Identifiers

PMID37760625
PMCPMC10526505
OpenAlexW4386929769

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.