Abstract W P335: Variation and Trends in the Documentation of National Institutes of Health Stroke Scale (NIHSS) Among GWTG-Stroke Hospitals
Bibliographic record
Abstract
Background: Despite its clinical importance NIHSS is often not documented in routine practice and in stroke registries. We describe trends in NIHSS documentation by GWTG-Stroke hospitals, the relationship between hospital documentation rates and reported NIHSS, and identify hospital- and patient-level factors associated with documentation. Methods: We analyzed NIHSS documentation in 1,159,981 acute ischemic stroke patients admitted to 1,682 GWTG-Stroke hospitals between 2003-2012. We used multivariable logistic regression models to identify hospital- and patient-level predictors of NIHSS documentation. Because of rapid increases in NIHSS documentation in recent years, multivariable analyses were restricted to the 2011-2012 period. Results: The overall NIHSS documentation rate was 51% and the mean NIHSS score was 6.8. Between 2003 -2012 mean hospital-level NIHSS documentation increased from 27% to 70%. The mean hospital-level NIHSS documentation rates and NIHSS score were inversely related (r = -0.228, p <0.0001), but this relationship was driven primarily by hospitals with documentation rates of<20% (Figure). Multivariable analysis indicated that NIHSS documentation was impacted by arrival mode, last known well-to-arrival time, and was higher at primary stroke centers and hospitals with larger case volumes (Table). Conclusion: Evidence of selection bias in reporting NIHSS was limited to hospitals with low documentation rates. Documentation of NIHSS was influenced by both hospital and patient-level factors and was higher in patients who were candidates for thrombolysis treatment. Documentation of NIHSS has improved dramatically in recent years.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".