Bibliographic record
Abstract
Background Fine needle aspiration (FNA) of adrenals is needed in patients with pyrexia of unknown origin (PUO) and adrenal enlargement in the absence of other diagnostic clues. Adrenals are easily accessible by endoscopic ultrasound (EUS) due to proximity; however, there is no systemic study available. The aim of this study was to evaluate the diagnostic yield and safety of EUS-FNA of enlarged adrenal in patients with pyrexia of unknown origin (PUO). Methods Data were analysed from October 2010 to September 2016 at a single tertiary care centre in North India. EUS FNA of enlarged adrenals was done in fifty-two patients for the etiological diagnosis of PUO in whom a definitive diagnosis could not be made with other means. Results The mean age was 48±14 years; 36 were males, and 16 were females. EUS-FNA was done from left adrenal in 50 patients and from right adrenal in 2 patients. Technical success was achieved in 100% cases. The 19 G needle was used in majority (75%) due to the presence of necrotic areas in adrenals; median numbers of passes were 2. The cytopathological diagnoses were tuberculosis (n=36), histoplasmosis (n=13), lymphoma (n=2), and metastasis from undiagnosed neuroendocrine tumour of lung (n=1). Thus a diagnosis could be made in 52/52 (100%) patients. None of the patients had any procedure-related complications. Conclusions EUS-FNA is a safe and effective method for evaluating aetiology of PUO in patients with adrenal enlargement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".