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Spinal cord stimulation for refractory angina pectoris: a case series

2023· article· en· W4387174045 on OpenAlexaboutno aff
D.D. Duse, V. Ya. Babchenko, Roman Kiselev, Vladimir Murtazin

Bibliographic record

VenuePatologiya krovoobrashcheniya i kardiokhirurgiya · 2023
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scaleAnginaQuality of life (healthcare)Refractory (planetary science)Spinal cord stimulationPhysical therapySpinal cordInformed consentAnesthesiaCanadian Cardiovascular SocietyStimulationInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Background: Refractory angina pectoris significantly reduces the length and quality of life. One of the methods to control symptoms and improve the quality of life of patients with refractory angina pectoris is spinal cord stimulation.Objective: To evaluate the effectiveness of spinal cord stimulation in controlling anginal pain in the long-term period over a long follow-up (>7 years).Methods: We retrospectively studied treatment results of 9 patients (6 men and 3 women) in the long-term follow-up from October 2012 to November 2022. Anginal pain and patients' quality of life were assessed using the visual analog scale and the Seattle Angina Questionnaire, respectively.Results: The mean follow-up was 7.33 ± 1.11 years. In the long-term postoperative period, regression of pain according to the visual analog scale was 52.3% (P = .0025). The Seattle Angina Questionnaire showed an improvement of the quality of life by 52.2% (P = .0993).Conclusion: Spinal cord stimulation allows to make control of chronic anginal pain more efficient, improve patients’ length and quality of life, and reduce the frequency and severity of disability. Received 8 December 2022. Revised 17 July 2023. Accepted 18 July 2023. Informed consent: The patient’s informed consent to use the records for medical purposes is obtained. Funding: The study did not have sponsorship. Conflict of interest: The authors declare no conflict of interest. Contribution of the authors: The authors contributed equally to this article.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0050.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.054
GPT teacher head0.337
Teacher spread0.283 · 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 designCase report
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

Citations1
Published2023
Admission routes1
Has abstractyes

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