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Record W1962996465 · doi:10.14740/jnr.v5i4-5.352

Nutech Functional Score: A New Functional Scoring System for Patients With Cerebrovascular Accidents

2015· article· en· W1962996465 on OpenAlexvenueno aff
Geeta Shroff

Bibliographic record

VenueJournal of Neurology Research · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineStroke (engine)Physical medicine and rehabilitationScale (ratio)Physical therapyMeasure (data warehouse)Data miningComputer scienceCartography

Abstract

fetched live from OpenAlex

Background: Stroke or cerebrovascular accident happens when a part of the brain gets poor supply of blood that leads to either hemorrhage or blockage due to deoxygenation. To measure stroke outcomes, various stroke scales are invented in the last few decades. The present study has compared a novel stroke scale, Nutech functional score (NFS) to that with the most widely used and globally recognized European stroke scale (ESS). Methods: NFS has been designed with 22 different parameters taking into consideration its ability to measure neurological as well as functional impairments associated with stroke. Each symptom has been assigned scores (1, 2, 3, 4, 5) that runs in direction from extreme bad (1) to normal (5). The scores are converted to numeric values using an empirical formula. We have compared both the NFS and ESS scores for measuring stroke outcomes. Results: NFS is able to assess motor, sensory and autonomic parameters for stroke patients. It assesses not only the clinical symptoms but the overall change in quality of life. ESS fails to measure all kinds of functional parameters and it involves tedious calculations. Conclusion: NFS has proven to be a much simpler and efficient scoring system in comparison with ESS which is a 14-point stroke scale. NFS is a multiple use scale which can measure more parameters than ESS and can be used universally to assess the patients suffering with stroke. J Neurol Res. 2015;5(4-5):246-251 doi: http://dx.doi.org/10.14740/jnr352w

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.228
Threshold uncertainty score0.433

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.126
GPT teacher head0.332
Teacher spread0.207 · 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 teacher head, 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
Published2015
Admission routes1
Has abstractyes

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