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Record W4403579571 · doi:10.1186/s43045-024-00469-8

Socio-demographic, clinical variables and pain among Egyptian patients of opioid use disorder with and without comorbid gabapentin use

2024· article· en· W4403579571 on OpenAlexaboutno aff
Nahla Nagy, Nesreen Mohsen, Hanan Elrassas, Nourhan Dardeer, Sara Abdelkader Elawady

Bibliographic record

VenueMiddle East Current Psychiatry · 2024
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsGabapentinMedicineOpioid use disorderOpioidInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Abstract Introduction There is increased clinical use of gabapentin, these trends appear to chronologically align with the opioid overdose crisis and particularly with the increased restrictions on opioid prescription. Objectives The study aimed to assess the clinical variables, pain quality, and severity among patients with opioid use disorder as the main substance with and without gabapentin comorbid use. Methods The research involved 60 patients diagnosed with substance use disorder, divided into two groups: opioid use as the main substance and opioid use with comorbid gabapentin. All participants were assessed using a semi-structured clinical addiction interview, the structured clinical interview for diagnostic and statistical manual of mental disorders, the Addiction Severity Index, urine drug test, McGill Pain Questionnaire, and Douleur Neuropathique en 4 Questionnaire, in one interview setting. Results The study showed that pain relief was the main desirable effect in the gabapentin group, with 66.2% of the group experiencing it. Euphoria was the desirable effect in the opioid group, while overdose was more common in the gabapentin group. Respiratory complications were also more common in the Gabapentin group. Conclusion The study concluded that patients with comorbid gabapentin use disorder had a higher frequency of overdose, history of pain, and significant neuropathic pain, while less abstinence.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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