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Record W2262519714 · doi:10.1161/str.43.suppl_1.a2484

Abstract 2484: Validity and Reliability of Retrospective Scoring of the Pediatric NIH Stroke Scale

2012· article· en· W2262519714 on OpenAlexaff
Lauren A. Beslow, Scott E. Kasner, Sabrina E. Smith, Michael T. Mullen, Matthew P. Kirschen, Rachel A Bastian, Michael M. Dowling, Warren Lo, Lori C. Jordan, Timothy J. Bernard, Neil Friedman, Gabrielle deVeber, Adam Kirton, Lisa Abraham, Daniel J. Licht, Abbas F. Jawad, Jonas H. Ellenberg, Ebbing Lautenbach, Rebecca Ichord

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsMedicineRetrospective cohort studyMedical recordProspective cohort studyStroke (engine)Physical therapyPediatricsSurgery

Abstract

fetched live from OpenAlex

Background and Objectives: The Pediatric National Institutes of Health Stroke Scale (PedNIHSS), an adaptation of the adult NIH Stroke Scale, is a quantitative measure of stroke severity shown to be reliable when scored prospectively. The ability to calculate the PedNIHSS score retrospectively would be invaluable in the conduct of retrospective pediatric stroke studies. To this end, the objective of this study was to assess the validity and reliability of calculating the PedNIHSS score retrospectively from medical records. Methods: Neurological examinations documented in medical records of 75 children from 9 institutions were deidentified and photocopied. All subjects had been previously enrolled in a prospective PedNIHSS validation study. Four neurologists of varying clinical training levels were given detailed instructions on how to translate the neurological examination into a PedNIHSS score. If an item was not recorded in the medical record, it was scored as 0 (normal) as was done in past adult studies. The raters were blinded to the PedNIHSS scores derived from the prospective study, reviewed the documented neurological examinations, and retrospectively assigned the PedNIHSS score. Each rater scored the 75 patients' examinations in the same order. Retrospective scores were compared among raters and to the prospectively measured scores. Results: The mean prospective total PedNIHSS score for the 75 subjects was 8.2 (SD 7) with median 6 (IQR 3-12).The mean total retrospective PedNIHSS score was 7.6 (SD 7) with median 5 (IQR 3-11). The mean and median total prospective and retrospective PedNIHSS scores were not significantly different (p= 0.49 Student's t-test; p=0.37 Wilcoxon rank-sum). Total retrospective PedNIHSS scores correlated highly with prospectively assigned total scores (R 2 0.76, p<0.001). Eighty-nine percent of retrospective total scores were within 5 points of the prospectively scored totals. Using a pre-determined threshold PedNIHSS score ≤5, the sensitivity of retrospective assessment was 87% (95% CI: 81-92%) and the specificity was 81% (95%CI: 74-87%). Interrater reliability for the total retrospective scores among the four raters assessed with the intraclass correlation coefficient was 0.95 (95% CI: 0.94-0.97). Interrater reliability for the 15 item scores that comprise the total PedNIHSS score, assessed with weighted κ, ranged from 0.47 to 0.93. Interrater reliability for all but one item was “substantial” or “excellent.” Conclusions: The PedNIHSS score can be assessed retrospectively from medical records with a high degree of validity and reliability. This tool, a stroke severity measure, can be used to improve the quality of retrospective pediatric stroke studies.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.043
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.021
GPT teacher head0.279
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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