Bibliographic record
Abstract
Background: Pheochromocytomas are rare, with a reported incidence of 2 - 8 cases per million persons, per year. The clinical presentation of pheochromocytomas is highly variable, making diagnosis challenging. Occasionally, the potent effects of the catecholamines can also cause “pheochromocytoma crisis” which can be fatal with around 800 deaths in the United States annually. The aim of this project is to study the clinical presentation, sensitivity of biochemical tests and imaging studies, preoperative work-up, pathological findings and surgical results of patients with pheochromocytoma. Methods: A prospective adrenalectomy database was reviewed from 1995 to 2009. The sub-group of patients with pheochromocytomas was identified. Their clinical findings, serum and urinary biochemistry results, imaging results, pre-op medical treatments, surgical approach, histopathology, and complications were reviewed. Results: A total of 88 patients with 85 having adrenalectomy were identified. Of these 24 had pheochromocytomas with a mean age of 50 (range: 19 - 75); 14 males and ten females. Elevated urinary catecholamines ( > 2 × upper normal limit) were identified in 90% of cases whilst CT imaging accurately localized all lesions (22 adrenal, 2 extra-adrenal). Pre-op medication: 75% (n = 18) required only single alpha-blockade agent, 25% (n = 6) of them combined alpha and beta-blockade. Surgery: 18 of the 22 adrenalectomies were attempted laparoscopically with 15 (83%) completed laparoscopically and the remainder (three) converted to open surgery due to adhesions or large tumor size. Four adrenalectomies were performed as planned open cases (two bilateral; two with tumors > 7.5 cm). All patients had benign pheochromocytomas. One patient developed post-operative pneumonia and one patient had post-op hypotension needing inotropic support for two days. All patients are alive and well, with no evidence of recurrent disease (minimum two year follow-up). Conclusions: The majority of adrenalectomies may be safely performed laparoscopically with minimal complications. A multi-disciplinary approach contributes to successful outcomes with involvement extending beyond preoperative consideration of the diagnosis into accurate localization of lesions, medical treatment when appropriate, specialized anaesthetic involvement and intensive care support. doi: http://dx.doi.org/10.4021/jcs188w
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".