P.202 Management of subdural hematoma as a complication of shunting in normal pressure hydrocephalus
Bibliographic record
Abstract
Background: Subdural hematoma (SDH) is a serious complication of shunt surgery for normal pressure hydrocephalus (NPH). Since the introduction of adjustable valves, management strategy has changed significantly. Methods: A retrospective review of NPH patients treated in the Hydrocephalus and CSF Disorders Clinic over the past five years was conducted. A review of clinical materials and imaging identified 32 patients who developed SDH following shunt surgery for NPH. Results: Twenty-seven patients were male and five were female. Mean age was 74. All patients received programmable valves. Nineteen patients were diagnosed with SDH within six months of shunt insertion, with a mean duration to diagnosis of 48 days. Five required surgery. The remaining 14 patients were treated successfully with shunt adjustment. All patients returned to their clinical baseline. Thirteen of the 32 patients developed SDH after a period of six months, with a mean duration to diagnosis of 43 months. None required surgical intervention. Ten patients were treated with shunt adjustment. The remainder were observed. Conclusions: Close surveillance following shunt insertion, particularly within the first six months post-op, is essential to prevent significant clinical sequelae secondary to SDH. Programmable valves can play an important role in early SDH management.
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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 | 0.001 |
| 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) | 0.001 | 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".