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Sleep Disturbance in Patients With Chronic Low Back Pain

2006· article· en· W2089380774 on OpenAlexaboutno aff
Raúl Marin, Tamara Cyhan, Wendy Miklos

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2006
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexMedicineSleep disorderPhysical therapySleep (system call)Visual analogue scaleMcGill Pain QuestionnaireChronic painLow back painSleep qualityInsomniaPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVE: To document the relationship between sleep disturbance and chronic low back pain in patients referred to a physical medicine and rehabilitation clinic. DESIGN: This is a prospective cross-sectional survey of 268 patients 18 yrs or older being evaluated for low back pain of greater than 6 months at a tertiary medical center. The survey consisted of a 43-item composite form that contained the Short-Form McGill Pain Questionnaire (SF-MPQ); the Pittsburgh Sleep Quality Index (PSQI); a pain visual analog scale (VAS); and questions regarding bed type, sleep position, and patients' sleep description. RESULTS: There was a significant relationship between pain and sleep (P<0.0005) with a 55% increase in the proportion of subjects reporting restless/light sleep after pain onset. There was no corresponding increase in sleep medication use. There was a significant direct correlation between SF-MPQ and PSQI (r=0.44, P<0.0005); between PSQI and VAS (r=0.41, P<0.0005); and between overall quality of sleep and VAS (r=0.31, P<0.0005). Finally, PSQI scores were the worst in subjects sleeping on an orthopedic mattress (P=0.001). CONCLUSIONS: Chronic low back pain significantly affects quality of sleep. Sleep problems should be addressed as an integral part of the pain management plan.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.002
GPT teacher head0.235
Teacher spread0.233 · 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

Citations185
Published2006
Admission routes1
Has abstractyes

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