External Validation and Modification of Nationwide Inpatient Sample Subarachnoid Hemorrhage Severity Score
Bibliographic record
Abstract
BACKGROUND: The Nationwide Inpatient Sample Subarachnoid Hemorrhage (SAH) Severity Score (NIS-SSS) was developed as a measure of SAH severity for use in administrative databases. The NIS-SSS consists of International Classification of Diseases Ninth Revision (ICD-9) diagnostic and procedure codes derived from the SAH inpatient course and has been validated against the Hunt-Hess score (HH). OBJECTIVE: To externally validate both the NIS-SSS and a modified version of the NIS-SSS (m-NIS-SSS) consisting of codes present only on admission, against the HH in a Canadian province-wide registry and administrative database of SAH patients. METHODS: A total of 1467 SAH patients admitted to Ontario stroke centers between 2003 and 2013 with recorded HH were included. The NIS-SSS and m-NIS-SSS were validated against the HH by testing correlation between the NIS-SSS/m-NIS-SSS and HH, comparing discriminative ability of the NIS-SSS/m-NIS-SSS vs HH for poor outcome by calculating area under the curve (AUC), and comparing calibration of the NIS-SSS, m-NIS-SSS, and HH by plotting predicted vs observed outcome. RESULTS: Correlation with HH was 0.417 (P ≤ .001) for NIS-SSS, and 0.403 (P ≤ .001) for m-NIS-SSS. AUC for prediction of poor outcome was 0.786 (0.764-0.808) for HH, 0.771 (0.748-0.793) for NIS-SSS, and 0.744 (0.721-0.767) for m-NIS-SSS. Calibration plots demonstrated that HH had the most accurate prediction of outcome, whereas the NIS-SSS and m-NIS-SSS did not accurately predict low risk of poor outcome. CONCLUSION: The NIS-SSS and m-NIS-SSS have good external validity, and therefore, may be suitable to approximate traditional clinical scores of disease severity in SAH research using administrative data.
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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".