Approach to the Patient With Pituitary Adenoma Using Telemedicine: Knowledge Gaps and Recommendations
Bibliographic record
Abstract
Telemedicine (TM) can be defined as the practice of medicine using technology such as video teleconferencing, telephone calls, or emails in order to provide health care from a distance. The emergence of the coronavirus disease 2019 (COVID-19) pandemic and its potential complications have created an urgent need for the transformation of physical visits into virtual encounters. Pituitary adenomas are one of the common endocrine tumors and can manifest with hormonal or mass effects. COVID-19 impacted the common pathway of diagnosis and treatment of these masses. TM utilization for pituitary adenomas has not been addressed thoroughly in the literature despite how common they are. In this review article, we discuss and implement what has been written in the literature about TM for pituitary adenoma alongside our suggested protocol. J Endocrinol Metab. 2023;13(2):43-48 doi: https://doi.org/10.14740/jem866
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".