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Record W2966910029 · doi:10.1136/bmjebm-2019-111236

Treating postoperative pain? Avoid tramadol, long-acting opioid analgesics and long-term use

2019· article· en· W2966910029 on OpenAlexaboutno aff
Georgia C. Richards

Bibliographic record

VenueBMJ evidence-based medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsTramadolMedicineOpioidAnesthesiaTerm (time)Pain ladderAnalgesicInternal medicine

Abstract

fetched live from OpenAlex

A recent cohort study investigated ‘the risk of transitioning from acute to prolonged use’ of opioid analgesics in patients undergoing elective surgery. Patients given tramadol or long-acting opioids after discharge were at greater risk of prolonged opioid use than those who were given other short-acting opioids. ### EBM verdict EBM Verdict on: Chronic use of tramadol after acute pain episode: cohort study. BMJ 2019 May 14. doi: 10.1136/bmj.l1849. Strong pain-relieving medicines called opioids are commonly prescribed when patients are discharged from hospitals. However, pain after elective surgery is usually short-lived. This cohort study1 addresses an important question regarding the prolonged use of opioid analgesics after elective surgery in light of the opioid crisis in the USA and Canada and increased prescribing of opioids in high-income countries.2 Tramadol is both a weak mu-opioid receptor agonist and a serotonin and norepinephrine reuptake inhibitor. Its active metabolite, O -desmethyltramadol, is longer acting than tramadol itself and is a more potent mu-opioid receptor agonist. Responses to tramadol, therefore, vary according to the genotype of the main metabolising enzyme, CYP2D6.3 Tramadol has been …

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.036
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.345
Teacher spread0.286 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical · Commentary

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
Published2019
Admission routes1
Has abstractyes

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