Neurosurgeon academic impact is associated with clinical outcomes after clipping of ruptured intracranial aneurysms
Bibliographic record
Abstract
BACKGROUND: Surgeon-dependent factors such as experience and volume are associated with patient outcomes. However, it is unknown whether a surgeon's research productivity could be related to outcomes. The main aim of this study is to investigate the association between the surgeon's academic productivity and clinical outcomes following neurosurgical clipping of ruptured aneurysms. METHODS: We performed a post-hoc analysis of 3567 patients who underwent clipping of ruptured intracranial aneurysms in the randomized trials of tirilazad mesylate from 1990 to 1997. These trials included 162 centers and 156 surgeons from 21 countries. Primary and secondary outcomes were: Glasgow outcome scale score and mortality, respectively. Total publications, H-index, and graduate degrees were used as academic indicators for each surgeon. The association between outcomes and academic factors were assessed using a hierarchical logistic regression analysis, adjusting for patient covariates. RESULTS: Academic profiles were available for 147 surgeons, treating a total of 3307 patients. Most surgeons were from the USA (62, 42%), Canada (18, 12%), and Germany (15, 10%). On univariate analysis, the H-index correlated with better functional outcomes and lower mortality rates. In the multivariate model, patients under the care of surgeons with higher H-indices demonstrated improved neurological outcomes (p = 0.01) compared to surgeons with lower H-indices, without any significant difference in mortality. None of the other academic indicators were significantly associated with outcomes. CONCLUSION: Although prognostication following surgery for ruptured intracranial aneurysms primarily depends on clinical and radiological factors, the academic impact of the operating neurosurgeon may explain some heterogeneity in surgical outcomes.
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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 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".