Pre- and postoperative magnetic resonance imaging appearance of the normal residual pituitary gland following macroadenoma resection: Clinical implications
Bibliographic record
Abstract
BACKGROUND: To assess the relationship between the preoperative magnetic resonance imaging (MRI) appearance of the normal residual pituitary gland (NRPG) and pituitary functional outcome following transsphenoidal resection of pituitary macroadenomas. METHODS: We retrospectively reviewed the medical records of 100 consecutive patients with a pituitary macroadenoma, who underwent transsphenoidal resection. The preoperative configuration of the displaced NRPG was stratified as superior, superolateral or lateral. The extent of postoperative restitution of the NRPG was divided into four groups: Group 1 - normal residual gland or almost normal; Group 2 - more than 50% restitution; Group 3 - less than 50% of the normal residual gland; and Group 4 - barely visible or absent residual gland. The pre- and postoperative NRPG appearance was correlated with pituitary functional status. RESULTS: Preoperatively, the NRPG was identifiable in 79 patients, with extrasellar displacement in 53%. The displacement pattern was superior in 8%, superolateral in 32%, and lateral in 58% of the patients. If the NRPG was displaced laterally, the ipsilateral cavernous sinus was not invaded by the pituitary macroadenoma. Partial or complete pituitary function was lost in 6 / 23 (26.1%) patients with superior or superolateral displacement of the NRPG, compared to only 1 / 36 (2.8%) patients without superior displacement of the NRPG (P = 0.025). Progressive postoperative reconstitution of the NRPG was related to the preservation of the pituitary hormonal axis (Pearson Chi-Square P < 0.001). CONCLUSIONS: Progressive displacement of the NRPG preoperatively, and lack of restitution of the NRPG on postoperative MRI appeared to correlate with the postoperative pituitary functional loss.
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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 | 0.003 |
| 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) | 0.001 | 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".