Development of two surgical approaches to the pituitary gland in the Horse
Bibliographic record
Abstract
BACKGROUND: Current treatment of equine pituitary pars intermedia dysfunction (PPID) requires daily oral medication. Minimally invasive surgical palliation of this condition is appealing as a single treatment to alleviate the clinical signs of disease, dramatically improving the welfare of the horse. OBJECTIVE: To develop a surgical approach to the equine pituitary gland, for subsequent treatment of PPID. STUDY DESIGN: A cadaver study to develop methodology and a terminal procedure under anaesthesia in the most promising techniques. ANIMALS AND METHODS: Four surgical approaches to the pituitary gland were investigated in cadaver animals. A ventral trans-basispheniodal osteotomy and a minimally invasive intravenous approach via the ventral cavernous sinus progressed to live horse trials. RESULTS: Technical complications prevented the myeloscopic and trans-sphenopalatine sinus techniques from being successful. The ventral basisphenoidal osteotomy was repeatable and has potential if an intra-operative imaging guidance system could be employed. The minimally invasive approach was repeatable, atraumatic and relatively inexpensive. CONCLUSIONS: A minimally invasive surgical approach to the equine pituitary gland is possible and allows for needle placement within the target tissue. More work is necessary to determine what that treatment might be, but repeatable access to the gland has been obtained, which is a promising step.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".