Bibliographic record
Abstract
With modern use of abdominal imaging, incidental detection of adrenal masses is increasingly common. These lesions are estimated to be present in 4% of all patients and in up to 10% of the elderly population. Fortunately, most adrenal masses are benign non‑functioning adenomas. However, some of these lesions are hyperfunctioning or harbour malignancy. A familiarity with the evaluation and management of incidental adrenal masses is of interest to endocrinologists as well as surgeons and primary care providers who order abdominal imaging tests. In 2023 a multidisciplinary working group of Canadian radiologists, endocrinologists, and radiologists published an updated guideline on the diagnosis, management, and follow‑up of the incidentally discovered adrenal mass.4 This publication has helped clarify the necessary imaging and biochemical testing required prior to creating a management plan for a patient with an incidental adrenal lesion. When faced with an adrenal mass, the clinician must answer 3 essential questions: 1) Is the mass benign or malignant? 2) Is the mass hormonally functional or non-functional? 3) How should the mass be managed?
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".