A Scoping Review of the Diurnal Variation in the Intensity of Neuropathic Pain
Bibliographic record
Abstract
BACKGROUND: Recent studies have suggested that neuropathic pain exhibits a daily diurnal pattern, with peak levels usually occurring in the late afternoon to evening and the trough in the morning hours, although literature on this topic has been sparse. This scoping review examines current evidence on the chronobiology of neuropathic pain both in animal models and in humans with neuropathic pain. METHODS: A literature search was conducted in major medical databases for relevant articles on the chronobiology of neuropathic pain both in animal models and in humans with neuropathic pain. Data extracted included details of specific animal models or specific neuropathic pain conditions in humans, methods and timing of assessing pain severity, and specific findings of diurnal variation in pain intensity or its surrogate markers. RESULTS: Thirteen animal and eight human studies published between 1976 and 2020 were included in the analysis. Seven of the 13 animal studies reported specific diurnal variation in pain intensity, with five of the seven studies reporting a trend toward increased sensitivity to mechanical allodynia or thermal hyperalgesia in the late light to dark phase. All eight studies in human subjects reported a diurnal variation in the intensity of neuropathic pain, where there was an increase in pain intensity through the day with peaks in the late evening and early night hours. CONCLUSIONS: Studies included in this review demonstrated a diurnal variation in the pattern of neuropathic pain that is distinct from the pattern for nociceptive pain. These findings have implications for potential therapeutic strategies for neuropathic pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".