Endoscopic Fenestration of A Symptomatic Cavum Septum Pellucidum:Technical Case Report
Bibliographic record
Abstract
OBJECTIVE: Cysts of the septum pellucidum (CSPs) may become symptomatic because of obstruction of cerebrospinal fluid flow, resulting in increased intracranial pressure and hydrocephalus requiring surgical intervention. Endoscopic fenestration may be the most effective and least invasive technique to treat this pathological condition. CLINICAL PRESENTATION: An 11-year-old boy sought treatment for frequent episodes of severe headache. On examination, he had papilledema. There was evidence on magnetic resonance imaging scans of a space-occupying CSP with obstructive hydrocephalus. INTERVENTION: The endoscopic technique of fenestration of both lateral walls of an enlarged CSP via a left frontal approach under ultrasound guidance using a rigid endoscope was successful. After surgery, the patient became asymptomatic, his papilledema resolved, and magnetic resonance imaging scans demonstrated collapse of the walls of the CSP toward the midline. CONCLUSION: Neuroendoscopic fenestration should be strongly considered as the treatment of choice for symptomatic CSPs. This procedure alone can lead to complete resolution of clinical symptoms and hydrocephalus, can reduce the size of the CSP, and can obviate the need for an implantable cerebrospinal fluid shunt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".