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Record W2378811391

The application of McGill pain questionnaire in diagnosis and treatment of trigeminal neuralgia

2010· article· en· W2378811391 on OpenAlexaboutno aff
Mingwei He

Bibliographic record

VenueZhongguo kangfu yixue zazhi · 2010
Typearticle
Languageen
FieldMedicine
TopicTrigeminal Neuralgia and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsTrigeminal neuralgiaMcGill Pain QuestionnaireMedicineNeuralgiaAnesthesiaNeuropathic painVisual analogue scale
DOInot available

Abstract

fetched live from OpenAlex

Objective: To observe the application of McGill pain questionnaire (MPQ) in discrimination and diagnosis of trigeminal neuralgia and assessment of therapeutic effect of radiofrequency thermocoagulation (RFT) on trigeminal neuralgia. Method: There were 159 trigeminal neuralgia patients enrolled in this study, of which 136 classic trigeminal neuralgia (CTN) patients and 23 mixed trigeminal neuralgia (MTN) patients. MPQ was used to identify the pain, and observe the pain relieving after RFT in 124 patients of which. Result: The mean present pain index (PPI) of CTN group was 4.20±0.34, and that of MTN group was 3.50±0.57 (P0.001); In pain rating index(PRI)-sensory subscale, the patients with CTN reported higher pain intensity in terms of sensory experience than patients with MTN (P0.001); The PRI-affective and PRI-evaluative dimensions of pain were significantly different between two groups, the scores were higher in CTN group (P 0.001). Overall, 103 (93.6% ) patients in CTN group responded to RFT with higher immediate improvements in self -report. In MTN group, only 10(58.8%) patients in MTN group responded to RFT with significant improvements in self-report. Conclusion: The application of MPQ could discriminate different type of trigeminal neuralgia well. In view of the different effectiveness in two type of trigeminal neuralgia after RFT, MPQ played an important role in diagnosis, discrimination and therapeutic effect assessment of trigeminal neuralgia.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.336
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.012
GPT teacher head0.280
Teacher spread0.267 · 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
Published2010
Admission routes1
Has abstractyes

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