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Record W1996676553 · doi:10.1016/j.pain.2005.10.007

Chronobiological characteristics of painful diabetic neuropathy and postherpetic neuralgia: Diurnal pain variation and effects of analgesic therapy

2005· article· en· W1996676553 on OpenAlexafffund
Mark Odrcich, Joan M. Bailey, Catherine M. Cahill, Ian Gilron

Bibliographic record

VenuePain · 2005
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsKingston General HospitalQueen's University
FundersCanadian Institutes of Health Research
KeywordsPostherpetic neuralgiaMedicineAnalgesicDiabetic neuropathyAnesthesiaNeuralgiaPain therapyNeuropathic painPhysical therapyDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

Clinical impressions suggest that neuropathic pain is often worse at night and significantly impairs sleep. However, the temporal pattern of neuropathic pain during waking hours has not been clearly characterized. Using clinical trial data, we have evaluated the diurnal variation of pain intensity before and during analgesic treatment in patients with diabetic neuropathy (DN) and postherpetic neuralgia (PHN). Pain intensity (0-10) measures throughout the day from a placebo-controlled trial of around-the-clock administration of gabapentin, morphine and a gabapentin-morphine combination in neuropathic pain patients were examined. Baseline data in untreated patients revealed no effect of day of week but a significant effect of time of day in both DN (P < 0.001) and PHN (P < 0.001) such that pain intensity progressively increases throughout the day. This temporal pattern is essentially preserved during treatment with gabapentin, morphine and their combination. Neuropathic pain intensity progressively increases throughout the day and this temporal profile appears to be unaffected by treatment with gabapentin and/or morphine. Advancing our understanding of the chronobiology of neuropathic pain may shed new light on various neurohormonal and neurophysiologic influences and lead to the identification of novel therapeutic targets. Furthermore, recognizing diurnal pain patterns may guide treatment strategies such as the targeted timing of analgesic therapies.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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.

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

Citations104
Published2005
Admission routes2
Has abstractyes

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