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Record W2901916500 · doi:10.1002/hed.25513

<sup>18</sup>F‐FDG PET/CT for locoregional surveillance following definitive treatment of head and neck cancer: A meta‐analysis of reported studies

2018· review· en· W2901916500 on OpenAlexaff
Erin Wong, Adam A. Dmytriw, Eugene Yu, John Waldron, Lin Lu, Rouhi Fazelzad, John R. de Almeida, Patrick Veit‐Haibach, Brian O’Sullivan, Wei Xu, Shao Hui Huang

Bibliographic record

VenueHead & Neck · 2018
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineNuclear medicinePositron emission tomographyHead and neckRadiation therapyHead and neck cancerMeta-analysisRadiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Abstract Purpose To evaluate the performance of 18 F‐fluorodeoxy‐ d ‐glucose positron emission tomography‐computed tomography ( 18 F‐FDG PET/CT) in identifying local failure and regional failure following curative radiotherapy or surgery for head and neck squamous cell carcinoma. Methods A comprehensive literature search identified studies published between January 2010 and August 2016. Diagnostic performance of 18 F‐FDG PET/CT was evaluated for local failure/regional failure stratified by treatment‐to‐scan time interval of ≤3 versus &gt;3 months. Results Twenty‐four studies (2627 patients) were included. Compared to ≤3 months, 18 F‐FDG PET/CT performed &gt;3 months showed significantly improved sensitivity (87% vs 60%, P = 0.020) and specificity (93% vs 84%, P &lt; 0.001) for local failure. There was no significant difference in sensitivity (79% vs 56%, P = 0.100) or specificity (95% vs 97%, P = 0.35) for regional failure &gt;3 versus ≤3 months. Conclusions This meta‐analysis confirms high specificity but modest sensitivity of posttreatment 18 F‐FDG PET/CT for local failure and regional failure. Sensitivity and specificity are significantly improved when 18 F‐FDG PET/CT is performed &gt;3 months for local failure.

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.

How this classification was reachedexpand

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 score0.556
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0130.005
Bibliometrics0.0010.001
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 head0.297
GPT teacher head0.459
Teacher spread0.161 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2018
Admission routes1
Has abstractyes

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