Diencephalic Syndrome: Misleading Clinical Onset of Low-Grade Glioma
Bibliographic record
Abstract
BACKGROUND: Diencephalic Syndrome is an atypical early manifestation of low-grade gliomas; so, it is important to detect it in patients that experience a failure to thrive despite adequate length growth and food intake. The purpose of this article is to focus attention on this rare but potentially dangerous cause of poor weight gain or stunting in childhood. MATERIALS AND METHODS: We describe four patients with Diencephalic Syndrome and low-grade gliomas who were evaluated in our institution from January 2017 to December 2021. CASE DESCRIPTION AND RESULTS: two patients presented with suspected malabsorption, and two presented with a suspected eating disorder. In all cases, neurological symptoms appeared late, explaining the reason for the diagnostic delay, which impacts negatively on prognosis and on quality of life. Currently, patients 1 and 2 have stable disease in second-line therapy, patient 3 has stable disease post end of second-line therapy, and patient 4 has stable disease in first-line therapy. Everyone is in psychophysical rehabilitation. CONCLUSIONS: A multidisciplinary evaluation is essential in order to make an early diagnosis and improve prognosis and 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".