Bibliographic record
Abstract
Patients determined to be neurologically deceased exhibit potentially harmful changes in various endocrine pathways due to disruptions of the body's neurohormonal control mechanisms. These deviations from endocrine homeostasis lead to hemodynamic, metabolic, and immunologic aberrations that are associated with reduced graft procurement and function for the purposes of organ donation. Existing literature has attempted to describe the pathophysiology that associates disruptions in endocrine pathways with organ dysfunction, both to increase understanding and to identify strategies to support the donor. For example, diabetes insipidus due to arginine vasopressin deficiency is commonly encountered, and should be anticipated. The significance of abnormalities in other pathways such as those involving cortisol and thyroid hormone is less established; however, there is increasing support for treating potential organ donors with combined hormonal therapies. While there are published documents aimed at guiding management of organ donors in general, many controversies exist and pediatric-specific literature is scarce. This article aims to review several of the important endocrine-specific aspects of managing the neurologically deceased organ donor, with an emphasis on pediatrics where information is available.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".