Abstracts from Hydrocephalus 2021: The Thirteenth Meeting of the International Society for Hydrocephalus and Cerebrospinal Fluid Disorders
Bibliographic record
Abstract
Introduction: Idiopathic normal pressure hydrocephalus (iNPH) can be treated with shunting.Little evidence exists to guide shunt selection, predictors of success, or patient follow-up.Methods: We performed retrospective reveiw of 82 consecutive iNPH patinets treated with shunting for iNPH between 2007 and 2018.Clinical factors included age, sex, Charlson Comorbidity Index (CCI), presence of hypertension and diabetes and follow-up.Surgical factors included pre-op spinal tap, type of shunt (LP, VP, fixed, adjustable), use of laparoscopic assistance and having surgery done by hydrocephalus specialist surgeon.Imaging factors included callosal angle (CA) and disproportional enlargement subarachnoid space hydrocephalus (DESH).Regressional statistics were performed.Results: 52 male and 30 female, patients were identified with average age 71.4 years.The cohort mRS improved from 3.84 to 2.66 post-operatively (p < 0.005).63.6% of patients had clinical improvement with shunt surgery in the short-term and 48.7% in the long-term.Factors that predicted better shunt outcome short-term were lower CCI ( < 0.05), absence of hypertension ( < 0.05), more intensive follow-up (< 0.05) and pre-op CA ≤ 80° (<0.05).Factors that predicted better shunt outcome long-term were younger age at surgery (< 0.05), use of laparoscopic approach (< 0.005) and pre-op DESH (< 0.05).Factors that predicted reduced complications were smaller pre-op CA (< 0.05), use of laparoscopic approach (< 0.05), utilization of LP shunt (< 0.05) and having surgery done by hydrocephalus specialist (< 0.05).Conclusion: In our centre, iNPH patients had improvement following shunting.Age, CCI, CA, DESH, more intensive follow-up, absence of hypertension and use of laparoscopic approach helped predict success of shunting in iNPH patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.011 |
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".