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Central Post Stroke Pain: Clinical, MRI, and SPECT Correlation

2011· article· en· W1498229876 on OpenAlexfundno aff
Jayantee Kalita, Bishwanath Kumar, U. K. Misra, Prasanta Pradhan

Bibliographic record

VenuePain Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsnot available
FundersMcGill University
KeywordsMedicineAllodyniaThalamusMagnetic resonance imagingStroke (engine)HyperalgesiaNuclear medicineRadiologyAnesthesiaInternal medicineNociception

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to report clinical spectrum of central post stroke pain (CPSP) and correlate these with magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) findings. DESIGN: The study was designed as a prospective study. SETTING: The study was set in a tertiary care teaching hospital. SUBJECT AND METHOD: Twenty-three consecutive CPSP patients were included and their severity of pain, sensory threshold, allodynia, hyperalgesia, and temporal summation were assessed by quantitative sensory testing (QST). Cranial MRI and (99)Tc ethylene cystine dimmer SPECT findings correlated with QST. RESULTS: The duration of CPSP was 5 months (0.25-108). Allodynia was present in 12 patients, punctuate hyperalgesia in 11, and temporal summation in 12. SPECT was abnormal on visual analysis in 17 patients; hypoperfusion in corresponding thalamus in nine, and parietal cortex in 11 patients. Semiquantitative analysis revealed hyperperfusion of thalamus in four and parietal cortex in five patients. MRI revealed infarction in 14 and hematoma in nine patients. The QST findings were similar in thalamic and extrathalamic CPSP. The MRI and SPECT findings were also not different in CPSP patients with and without allodynia. CONCLUSION: The QST findings in patients with CPSP were similar in patients with thalami and extrathalamic lesions. SPECT and MRI findings were also not different in CPSP patients with and without allodynia.

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.296
Teacher spread0.255 · 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.

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

Citations33
Published2011
Admission routes1
Has abstractyes

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