The network topology of aneurysmal subarachnoid haemorrhage
Bibliographic record
Abstract
OBJECTIVE: Network analysis is an emerging tool for the study of complex systems. In the current report, the cascade of physiological and neurological changes following aneurysmal subarachnoid haemorrhage (SAH) was modelled as a complex system of interacting parameters. Graph theoretical analysis was then applied to identify parameters at critical topological junctions of the network, which may represent the most effective therapeutic targets. METHODS: Correlation matrices were calculated using a combination of Pearson, polyserial and polychoric regressions among 50 variables collected from 120 participants (38 male; mean age 51 years) included in the CONSCIOUS-1 trial. Graph theoretical analysis was performed to identify important topological features within the network formed by the interactions among these variables. Non-parametric resampling was applied to determine thresholds for significance. RESULTS: Several critical network hubs were identified, including the incidence of delayed ischaemic neurological deficit (DIND), anaemia and hypoalbuminaemia/hypoproteinaemia. While not significant hubs, World Federation of Neurosurgical Societies (WFNS) score and use of rescue therapy had widespread connections within the network. Patient sex and history of hypertension also strongly clustered with other variables. A subnetwork (module) was also identified, which was related to neurological outcomes including WFNS score, angiographic vasospasm, DIND, use of rescue therapy and hydrocephalus. INTERPRETATION: Using graph theoretical analysis, we identify critical network topologies following SAH, which may serve as useful therapeutic targets. Importantly, we demonstrate that network analysis is a robust method to model complex interactions following SAH. TRIAL REGISTRATION NUMBER: URL: http://www.clinicaltrials.gov; Identifier: NCT00111085.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".