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Record W2899534382 · doi:10.1515/sjpain-2018-0317

The utility/futility of medications for neuropathic pain – an observational study

2018· article· en· W2899534382 on OpenAlexaffabout
Stephen Butler, Daniel Eek, Lena Ring, Allen Gordon, Rolf Karlsten

Bibliographic record

VenueScandinavian Journal of Pain · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsMedicineObservational studySpecialtyNeuropathic painPopulationNeurologyPhysical therapyDiabetic neuropathyPatient satisfactionDiabetes mellitusEmergency medicineFamily medicineInternal medicineSurgeryPsychiatryAnesthesia

Abstract

fetched live from OpenAlex

Background and aims The RELIEF (Real Life) study by AstraZeneca was designed as an observational study to validate a series of Patient Reported Outcome (PRO) questionnaires in a mixed population of subjects with neuropathic pain (NP) coming from diabetes, neurology and primary care clinics. This article is an analysis of a subset of the information to include the medications used and the effects of pharmacological treatment over 6 months. The RELIEF study was performed during 2010-2013. Methods Subjects were recruited from various specialty clinics and one general practice clinic across Canada. The subjects were followed for a total of 2 years with repeated documentation of their status using 10 PROs. A total of 210 of the recruited subjects were entered into the data base and analyzed. Of these, 123 had examination-verified painful diabetic neuropathy (PDN) and 87 had examination-verified post-traumatic neuropathy (PTN). To evaluate the responsiveness of the PROs to change, several time points were included and this study focusses primarily on the first 6 months. Subjects also maintained a diary to document all medications, both for pain and other medical conditions, including all doses, start dates and stop dates, that could be correlated to changes in the PRO parameters. Results RELIEF was successful in being able to correlate the validity of the PROs and this data was used for further AstraZeneca Phase 1, 2, and 3 clinical trials of NP. To our surprise, there was very little change in pain and low levels of patient satisfaction with treatment during the trial. Approximately 15% of the subjects reported improvement, 8% worsening of pain, the remainder reported pain unchanged despite the use of multiple medications at multiple doses, alone or in combination with frequent changes of medications and doses over the study. Those taking predominantly NSAIDs (COX-inhibitors) did no worse than those taking the standard recommended medications against NP. Conclusions Since this is a real-life study, it reflects the clinical utility of a variety of internationally recommended medications for the treatment of NP. In positive clinical trials of these medications in selected "ideal" subjects, the effects are not overwhelming - 30% are 50% improved on average. This study shows that in the real world the results are not nearly as positive and reflects information from non-published negative clinical trials. Implications We still do not have very successful medications for NP. Patients probably differ in many respects from those subjects in clinical trials. This is not to negate the use of recommended medications for NP but an indication that success rates of treatment are likely to be worse than the data coming from those trials published by the pharmaceutical industry.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.133
GPT teacher head0.383
Teacher spread0.250 · 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 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

Citations11
Published2018
Admission routes2
Has abstractyes

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