Predictors of Pituitary Dysfunction in Patients Surviving Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND: Stroke is a leading cause of death in industrialized countries, representing the main cause of long-term disability. Recent studies indicate that hypopituitarism may be observed after an acute stroke. OBJECTIVE: The aim was to prospectively investigate incidence and pattern of pituitary dysfunction in patients suffering ischemic stroke and to assess the predictive value of different clinical and radiological parameters for hypopituitarism. PATIENTS AND METHODS: We assessed endocrine, clinical, radiological, and functional parameters in 56 patients (34 males; mean age, 64.8 ± 1.3 yr; mean body mass index, 25.8 ± 0.45 kg/m(2)) at 1-3 months (visit 1) and 12-15 months (visit 2) after an ischemic stroke. RESULTS: At visit 1, hypopituitarism was detected in 20 (35.7%) of 56 stroke patients, with multiple deficits in three and isolated deficits in 17. At visit 2, hypopituitarism was detected in 18 (37.5%) of 48 stroke patients, with multiple deficits in two. Four patients with previously diagnosed isolated GH or LH/FSH deficit exhibited normal pituitary function, whereas GH deficiency was newly diagnosed in three cases. Hypopituitarism was associated with worse outcome. We identified both clinical (preexisting diabetes mellitus, medical complications during hospitalization) and radiological (Alberta Stroke Programme Early CT Score ≤ 7) parameters as major risk factors for developing hypopituitarism after ischemic stroke. CONCLUSIONS: Hypopituitarism may associate with ischemic stroke in one third of cases and persist in a long-term period, aggravating the functional outcome. We identified specific risk factors for hypopituitarism after stroke, which may help to select patients needing an accurate endocrine evaluation to improve stroke outcome.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".