Postoperative Screening With the Modified National Institutes of Health Stroke Scale After Noncardiac Surgery: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Perioperative stroke is associated with high rates of morbidity and mortality, yet there is no validated screening tool. The modified National Institutes of Health Stroke Scale (mNIHSS) is validated for use in nonsurgical strokes but is not well-studied in surgical patients. We evaluated perioperative changes in the mNIHSS score in noncardiac, non-neurological surgery patients, feasibility in the perioperative setting, and the relationship between baseline cognitive screening and change in mNIHSS score. METHODS: Patients aged 65 years and above presenting for noncardiac, non-neurological surgery were prospectively recruited. Those with significant preoperative cognitive impairment (Montreal Cognitive Assessment score [MoCA] ≤17) were excluded. mNIHSS was assessed preoperatively, on postoperative day (POD) 0, POD 1, and POD 2, demographic data collected, and feedback solicited from participants. Changes in mNIHSS from baseline, time to completion, and relationship between baseline MoCA score and change in mNIHSS score were analyzed. RESULTS: Twenty-five patients were enrolled into the study; no overt strokes occurred. Median mNIHSS score increased between baseline (0 interquartile range [IQR 0 to 1]) and POD 0 (2 [IQR 0 to 3.5]; P<0.001) but not between baseline and POD 1 (0.5 [IQR 0 to 1.5]; P=0.174) or POD 2 (0 [IQR 0 to 1]; P=0.650). Time to complete the mNIHSS at baseline was 3.5 minutes (SD 0.8), increasing to 4.1 minutes (SD 1.0) on POD 0 (P=0.0249). Baseline MoCA score was correlated with mNIHSS score change (P=0.038). Perioperative administration of the mNIHSS was feasible, and acceptable to patients. CONCLUSIONS: Changes in mNIHSS score can occur early after surgery in the absence of overt stroke. Assessment of mNIHSS appears feasible in the perioperative setting, although further research is required to define its role in detecting perioperative stroke.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".