Diagnostic accuracy outcomes of office‐based (outpatient) biopsies in patients with laryngopharyngeal lesions: A systematic review
Bibliographic record
Abstract
BACKGROUND: In-office biopsies (IOB) using local anaesthetic for laryngopharyngeal tumours has become an increasingly popular approach since the advent of distal chip endoscopes. Although a wide range of studies advocate use in clinical practice, the widespread application of the procedure is hampered by concerns regarding diagnostic accuracy. OBJECTIVE: To assess the diagnostic accuracy of IOB performed via flexible endoscopy. In addition, to analyse modifiable factors that may affect diagnostic accuracy of IOB. DESIGN: A systematic review following the PRISMA guidelines was conducted. PubMed, EMBASE, the Cochrane Library, Web of Science and CINAHL were used in the literature database search. Quality assessment of included studies was perfomed using the Newcastle-Ottawa Scale. RESULTS: A total of 875 studies were identified, 16 of which were included into the systematic review; 1572 successful biopsies were performed using flexible endoscopy; 1283 cases were accurately diagnosed in the outpatient setting (81.6%) and 289 samples did not provide an accurate diagnosis (18.4%). The median sensitivity of IOB was 73%, and the specificity was 96.7%. Analysis of variable factors did not show any significant differences in method of approach, size of equipment (forceps) and additional lighting system or learning curve. CONCLUSION: IOB are a viable tool for diagnostic workup of laryngopharyngeal tumours. Clinicians should be wary of reported limitations of IOB when benign or pre-malignant diagnoses are made. In cases suspicious of malignancy, confirmatory investigation should be conducted.
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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 | 0.013 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".