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Treatment of Chronic Pain by Long‐Acting Opioids and the Effects on Sleep

2010· review· en· W2112407557 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePain Practice · 2010
Typereview
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsCapital District Health Authority
Fundersnot available
KeywordsMedicineSleep (system call)Chronic painOpioidPopulationAnesthesiaPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Chronic pain affects a substantial part of the population, and conveys a huge economic cost to society. Owing to its prevalence and adverse impact, it is of particular interest to clinicians, patients, and the pharmaceutical industry. Conversely, the effects of pain on sleep, sleep on pain, and opioid analgesics on sleep represent a large gap in our understanding, even though pain and sleep are closely linked, inter-related conditions. Chronic pain is often treated by opioid analgesics, which are often thought to promote restful sleep. Indeed it may be assumed that by relieving pain, sleep quality will improve concomitantly. In fact, the reality is much more complicated. The effects of opioids vary according to their formulation and duration of action, and have diverse effects on sleep processes. Despite the prevalence of this problem, there is a surprising paucity of data on the effects of opioids on sleep. This review attempts to summarize the links between pain and sleep, and to look at the studies with opioid analgesics, particularly those with extended-release formulations, that have investigated the effects of opioid analgesics on sleep.

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.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
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 integrity0.0010.001
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 head0.014
GPT teacher head0.343
Teacher spread0.329 · 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