P.028 Surgical Treatment for Idiopathic Intracranial Hypertension – Strategy for the Better Management
Bibliographic record
Abstract
Background: Idiopathic intracranial hypertension (IIH) is a condition of increased intracranial pressure in the absence of a space-occupying lesion. The goal of this study is to investigate which factors may influence outcomes in order to improve surgical strategy. We hypothesized diabetes, hypertension, smoking, and obesity influence patients prognosis. Methods: This retrospective chart review included patients diagnosed with IIH who underwent surgical intervention. All patients receiving surgery between 2008 and 2018 were included, and divided into 2 cohorts. Cohort 1 representing favorable course and cohort 2 representing unfavorable course. Favorable course was defined as requiring single surgery for management. Unfavorable course required multiple surgical revisions. Results: Overall, 35/48 (73%) comprised the favorable group. Thirteen patients (27%) comprised the unfavorable group. Of the unfavorable group, 54% had LP shunts, with the remaining receiving VP shunts. There was no association between type of shunt and outcome. Common issues the unfavorable group encountered were persisting symptoms, infections, obstruction of shunt and replacement of shunt. Smoking and frequent follow-up were associated with unfavorable course. Gender, BMI, age, comorbidities and shunt type were not associated with outcome. Conclusions: We found smoking and patient follow-up had a significant association with unfavorable outcome. Other factors had no association with patient outcome.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".