Variation and Trends in the Documentation of National Institutes of Health Stroke Scale in GWTG-Stroke Hospitals
Bibliographic record
Abstract
BACKGROUND: Although National Institutes of Health Stroke Scale (NIHSS) is an important prognostic variable, it is often incompletely documented in clinical registries, such as Get With The Guidelines (GWTG)-Stroke. We describe trends in NIHSS documentation by GWTG-Stroke hospitals, identify patient-level and hospital-level factors associated with documentation, and determine the degree to which the reporting of NIHSS is potentially biased. METHODS AND RESULTS: We analyzed NIHSS documentation in 1 184 288 patients with acute ischemic stroke admitted to 1704 GWTG-Stroke hospitals between 2003 and 2012. We used multivariable logistic regression models to identify hospital-level and patient-level predictors of NIHSS documentation. We examined the relationship between hospital-level NIHSS documentation rates and observed NIHSS scores to determine whether the reporting of NIHSS data was subject to selection bias. The overall NIHSS documentation rate was 56.1%; the median NIHSS was 4 (interquartile range, 2-9). Between 2003 and 2012, mean hospital-level NIHSS documentation increased dramatically from 27% to 70% (P<0.0001). Documentation was higher in patients who arrived by ambulance, who arrived soon after onset, and were treated at larger, primary stroke centers. Hospital-level NIHSS documentation rates and NIHSS scores were modestly inversely correlated (r=-0.207; P<0.0001), suggesting that NIHSS data from hospitals with low documentation were shifted toward higher values. In sensitivity analysis, the degree of bias in NIHSS reporting was reduced in more recent years (2011-2012) when NIHSS documentation was noticeably better. CONCLUSIONS: Documentation of NIHSS is higher in patients who are thrombolysis candidates. Evidence of modest bias in NIHSS scores was observed but this has lessened as the documentation of NIHSS has improved in recent years.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".