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Record W2167355159 · doi:10.1177/1054773811406110

Evaluation of Night-Time Pain Characteristics and Quality of Sleep in Postoperative Turkish Orthopedic Patients

2011· article· en· W2167355159 on OpenAlexfundaboutno aff
Funda Büyükyılmaz, Merdiye Şendir, Rengin Acaroğlu

Bibliographic record

VenueClinical Nursing Research · 2011
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersMcGill University
KeywordsMedicineSleep qualityOrthopedic surgeryPittsburgh Sleep Quality IndexPhysical therapySleep (system call)McGill Pain QuestionnaireTurkishInsomniaSurgeryVisual analogue scalePsychiatry

Abstract

fetched live from OpenAlex

This descriptive, correlational study was conducted to determine orthopedic patients' night-time pain characteristics, their quality of sleep and the contributing factors to poor sleep experiences, and the relationship between pain and sleep. Data were collected by using the McGill Pain Questionnaire-SF (MPQ-SF) and Pittsburgh Sleep Quality Index (PSQI) on the second postoperative day. Data were analyzed using the SPSS version 10.0 for Windows. Mean age of the 75 patients was 49.55 ± 21.10 years and were hospitalized in the orthopedic wards for 10.56 ± 14.74 days. Of the sample, 65.3% were female and 36% had hip/knee arthroplasty surgery. Pain (45%) and noise (23%) were found to be the most cited factors affecting the sleep of patients in postoperative periods. They experienced "external" pain at the surgical site and verbalized their pain as "stabbing" and "tiring-exhausting." Patients' night-time pain was determined to be severe (6.59 ± 1.62); their quality of sleep was also poor (9.24 ± 3.53). A statistically significant correlation was found between patients' pain intensity and quality of sleep (p≤.05).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.268
GPT teacher head0.523
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations52
Published2011
Admission routes2
Has abstractyes

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