Diagnostic accuracy of fine needle aspiration biopsy for detection of malignancy in pediatric thyroid nodules: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Fine needle aspiration biopsy (FNAB) is an accurate test commonly used to determine whether thyroid nodules are malignant in adults. However, less is known about its diagnostic accuracy for this purpose in children, where conduct of FNAB is less frequent, more technically challenging, and pre-test probabilities of malignancy are often higher. The purpose of this systematic review is to evaluate the diagnostic accuracy of FNAB for the detection of malignancy in pediatric thyroid nodules. METHODS: We will search electronic bibliographic databases (MEDLINE, EMBASE, the Cochrane Library, and Evidence-Based Medicine) from their date of inception, reference lists of included articles, proceedings from relevant conferences, and the table of contents of the Journal of Pediatric Surgery (January 2007-present). Two reviewers will independently screen titles and abstracts and identify diagnostic accuracy studies involving FNAB of the thyroid in children. We will include studies comparing FNAB to a reference standard of surgical histopathology or clinical follow-up for detection of malignancy in pediatric thyroid nodules. Two investigators will independently extract data and assess risk of bias using the Quality of Diagnostic Accuracy Studies-II tool. Pooled estimates of sensitivity, specificity, and positive and negative likelihood ratios will be calculated using bivariate random-effects and hierarchical summary receiver operating characteristic models. In the presence of between-study heterogeneity, we will conduct stratified meta-analyses and meta-regression to determine whether diagnostic accuracy estimates vary by country of origin, use of ultrasound guidance during FNAB, qualifications of the individuals performing/interpreting FNAB, adherence to the Bethesda criteria for cytology classification, length of clinical follow-up, timing of data collection, patient selection methods, and presence of verification bias. DISCUSSION: This meta-analysis will determine the diagnostic accuracy of FNAB for detection of malignancy in pediatric thyroid nodules and explore whether heterogeneity observed across studies may be explained by variations in patient population, FNAB technique or interpretation, and/or study-level risks of bias. This will be the first study to determine the accuracy of Bethesda cytological classification levels of FNAB (benign, atypical, follicular, suspicious, malignant). We expect that our results will help in guiding clinical decision-making in children with thyroid nodules. SYSTEMATIC REVIEW REGISTRATION: PROSPERO No. CRD42014007140.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.113 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".