Distortion of Normal Pituitary Structures in Sellar Pathologies on MRI
Bibliographic record
Abstract
OBJECTIVE: This study was undertaken to assess the displacement patterns and shifts of the normal pituitary gland in sellar pathologies on MRI and to determine if the position of the bright spot (BS) represents a predicting factor for the position of the residual adenohypophysis (RAH) in pathological conditions. METHODS: In a control group of 102 patients without any pituitary pathology, the presence of the BS was evaluated. In 100 patients with intra- or suprasellar pathologies, presence and respective topography of BS and RAH were scrutinized on MRI, according to lesion type, size, endocrine status and intra-operative findings in the surgical group. RESULTS: The BS was visible in 91.2% of 102 cases in the control group, as compared to 75 of the 100 patients with sellar lesions. Location of RAH was identified in 58% of the patients, and RAH enhanced more than the lesion in all cases after contrast infusion. The RAH was identified in 65.3% of the 75 "BS positive" patients, as compared to 36% of the 25 "BS negative". The normal residual gland was visualized intra-operatively in 63.5% of the 52 operated patients: in 37 "BS positive" patients, it was visualized intra-operatively in 81.1% and in 28 "RAH positive" patients, it was identified in 82.1%. CONCLUSIONS: The BS can be identified in the majority and RAH in more than half of the cases with pituitary lesions on MRI. Positions of both the BS and RAH help predict the location of the normal residual gland during surgery and, therefore, may contribute to preserving the pituitary function.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".