The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Reliability and Validity of Estimating the NIH Stroke Scale Score from Medical Records
Abstract
Background and Purpose —The aim of our study was to determine whether the National Institutes of Health Stroke Scale (NIHSS) can be estimated retrospectively from medical records. The NIHSS is a quantitative measure of stroke-related neurological deficit with established reliability and validity for use in prospective clinical research. Recently, retrospective observational studies have estimated NIHSS scores from medical records for quantitative outcome analysis. The reliability and validity of estimation based on chart review has not been determined. Methods —Thirty-nine patients were selected because their NIHSS scores were formally measured at admission and discharge. Handwritten notes from medical records were abstracted and NIHSS scores were estimated by 6 raters who were blinded to the actual scores. Estimated scores were compared among raters and with the actual measured scores. Results —Interrater reliability was excellent, with an intraclass correlation coefficient of 0.82. Scores were well calibrated among the 6 raters. Estimated NIHSS scores closely approximated the actual scores, with a probability of 0.86 of correctly ranking a set of patients according to 5-point interval categories (as determined by the area under the receiver-operator characteristic curve). Patients with excellent outcomes (NIHSS score of ≤5) could be identified with sensitivity of 0.72 and specificity of 0.89. There were no significant differences between these parameters at admission and discharge. Conclusions —For the purposes of retrospective studies of acute stroke outcome, the NIHSS can be abstracted from medical records with a high degree of reliability and validity.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Statistical Methods for Rates and Proportions
Fleiss, J. L., Levin, B., & Paik, M. C. (2003) · Wiley Series in Probability and Statistics · doi:10.1002/0471445428 · Cited by 18,646
Cited by
Demaerschalk, B. M., Kleindorfer, D. O., Adeoye, O. M., Demchuk, A. M., Fugate, J. E., Grotta, J. C., Khalessi, A. A., Levy, E. I., Palesch, Y. Y., Prabhakaran, S., et al. (2016) · Stroke · doi:10.1161/str.0000000000000086 · Cited by 670
Bernhardt (2004) · doi:10.1161/01.str.0000120727.40792.40 · Cited by 593
Schwamm, L. H., Holloway, R. G., Amarenco, P., Audebert, H. J., Bakas, T., Chumbler, N. R., Handschu, R., Jauch, E. C., Knight, W. A., Levine, S. R., et al. (2009) · Stroke · doi:10.1161/strokeaha.109.192360 · Cited by 449
Anxiety After Stroke: The Importance of Subtyping
Chun, H.-Y. Y., Whiteley, W. N., Dennis, M. S., Mead, G. E., & Carson, A. J. (2018) · Stroke · doi:10.1161/strokeaha.117.020078 · Cited by 172
Wouters, A., Nysten, C., Thijs, V., & Lemmens, R. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00308 · Cited by 162
Metadata
- Authors
- Kasner, S. E., Chalela, J. A., Luciano, J. M., Cucchiara, B. L., Raps, E. C., McGarvey, M. L., Conroy, M. B., & Localio, A. R.
- Venue
- Stroke
- Published
- 1999-08
- Volume
- 30
- Issue
- 8
- Pages
- 1534-1537
- ISSN
- 0039-2499 (ISSN-L)
- 1524-4628
- OpenAlex
- W2112146349
- Cited by
- 405
- References
- 1
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.