Determinants of Quality of Life Improvement after Pituitary Surgery in Patients with Acromegaly
Bibliographic record
Abstract
Background: Acromegaly is a rare and slowly progressive growth disorder caused by the excessive secretion of Growth Hormone (GH). The vast majority of patients with acromegaly have a slow-growing pituitary tumor which may ultimately cause neurological deficits. However, the pleiotropic effects of GH on many organs causes a myriad of clinical comorbidities, disfigurement and pre-mature mortality. Tumor resection remains the primary treatment modality and good biochemical control is generally reported. However, it is not clear which factors have the greatest impact on quality of life (QoL) after surgery. Hypothesis: Improvement in QoL is reported rapidly after surgery. This improvement is not driven by biochemical cure of acromegaly. Methods: A series of 55 patients with acromegaly treated by a single surgeon at a single institution between 2002–2015 were asked to complete a previously validated quality of life questionnaire, the SF-36. The scores were averaged and compared pre-operatively and at two time-points post-operatively. The impact of various variables on quality of life will be assessed Results: Initial analysis of the data reveals significant improvement in patients’ perceived general health post-operatively. The same trend is observed across various measures of quality of life. Most of the improvement occurs in the early post-operative period. Further analysis will determine the most important factors affecting post-operative quality of life.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".