Assessing the Accuracy, Safety, and Tolerance of Office‐Based Endoscopic Biopsies for Laryngopharyngeal Lesions
Bibliographic record
Abstract
OBJECTIVE: The increasing prevalence of office-based biopsies (OBBs) for diagnosing laryngopharyngeal lesions underscores the need for a comprehensive evaluation of their clinical utility. This study aims to investigate the accuracy, safety, and tolerance of these procedures in an office setting. METHODS: We conducted a retrospective analysis of 490 OBBs performed with distal chip, working channel endoscopes. Histologic accuracy was assessed by comparing OBB results with operating room biopsies or, for benign lesions, by monitoring endoscopic findings over time. RESULTS: The majority of OBBs were taken primarily from the glottic larynx (52.4%), supraglottic larynx (17.3%), and base of tongue (14.5%). Procedural intolerance led to noncompletion in 4.1% of cases due to gag reflex (17 cases) and laryngospasm (3 cases); no serious complications were reported. OBBs guided management in 88.4% of cases. Histologically, 33.3% of cases were benign, 27.6% pre-malignant, 37.6% malignant, and 1.5% yielded inadequate specimens. Thirteen lesions (8.3%) initially identified as benign and 37 pre-malignant lesions (28.5%) were found to be malignant upon further biopsy. For invasive malignancies/severe dysplasia, OBBs showed a sensitivity of 89.4%, specificity of 95.8%, positive predictive value of 97.4%, negative predictive value of 83.4%, and accuracy of 91.7%. CONCLUSION: Office-based biopsies of laryngopharyngeal lesions are safe, generally well-tolerated, and offer reliable diagnostic results in appropriate clinical settings. Severe dysplasia or carcinoma in situ identified on OBB should prompt suspicion for invasive malignancy.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".