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Record W4402581552 · doi:10.1089/thy.2024.0313

Incidental Thyroid Nodules on Computed Tomography: A Systematic Review and Meta-Analysis Examining Prevalence, Follow-Up, and Risk of Malignancy

2024· review· en· W4402581552 on OpenAlex
Zhixing Song, Christopher Wu, Júlia Adriana Kasmirski, Andrea Gillis, Jessica Fazendin, Brenessa Lindeman, Herbert Chen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThyroid · 2024
Typereview
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMalignancyThyroid nodulesComputed tomographyRadiologyIncidentalomaMeta-analysisThyroid cancerThyroidInternal medicine

Abstract

fetched live from OpenAlex

Background: The increased utilization of computed tomography (CT) has led to a higher detection rate of thyroid incidentalomas. Currently, there are no widely agreed-upon guidelines for managing these incidentalomas. This study aims to investigate the prevalence, follow-up practices, and malignancy rates of thyroid incidentalomas detected by CT. Methods: We conducted a comprehensive search of PubMed, Embase, and Cochrane databases to identify relevant studies published before April 12, 2024 (PROSPERO #42024535501). Studies reporting on the prevalence, follow-up, and risk of malignancy (ROM) of thyroid incidentalomas detected by CT were included. Combined outcomes were analyzed using pooled proportion with a random-effects model. The risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials (RoB 2) and the Newcastle–Ottawa Scale tool. Subgroup analyses were conducted based on characteristics including size of the incidentaloma, CT area, and age of the study population. Results: Thirty-eight studies involving 195,959 patients were included in the prevalence analysis, revealing a prevalence of thyroid incidentalomas on CT of 8.3% (confidence interval [CI], 7.4–9.3). The prevalence was higher in neck CT (16.5%, CI, 11.0–22.1) compared with chest CT (6.6%, CI, 5.3–7.9). Multiple incidentalomas were found in 27.0% (CI, 12.9–41.1) of patients. Of the nodules, 46.3% (CI, 32.3–60.3) were ≥1 cm, and 28.6% (CI, 19.9–37.3) were ≥1.5 cm. Thyroid ultrasounds, biopsies, and surgeries were performed in 34.9% (CI, 26.1–43.7), 28.4% (CI, 19.9–36.9), and 8.2% (CI, 2.1–14.4) of cases, respectively. Additionally, 25 studies with 6272 patients reported a ROM of 3.9% (CI, 3.0–4.9) for thyroid incidentalomas detected on CT. A higher ROM was observed in incidentalomas ≥1 cm (11.7%, CI, 3.9–19.4) and ≥1.5 cm (24.9%, CI, 0–52.7) compared with those <1 cm (0.1%, CI, 0–0.8) and <1.5 cm (0%, CI, 0–0.2). Conclusions: Most thyroid incidentalomas identified on CT are benign. Implementing a collaborative protocol between radiologists and thyroid specialists to manage high-risk thyroid incidentalomas can ensure appropriate follow-up and optimal patient care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.578
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0130.004
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.331
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it