The effect of insomnia on physical therapy outcomes in patients with cervical and lumbar pain in clinical practice
Bibliographic record
Abstract
Nesanica je jedan od najčešće prijavljenih komorbiditeta kod hroničnog bola u kičmi. Međutim, osnovni mehanizmi koji objašnjavaju odnos između sna i bola još uvek nisu u potpunosti shvaćeni. Cilj istraživanja je bio da utvrdi razlike u ishodima fizikalne terapije i kvaliteta života kod pacijenata sa cervikalnim i/ili lumbalnim bolom koji imaju nesanicu u odnosu na pacijente bez nesanice tokom dvonedeljnog perioda aktivnog tretmana u uslovima rutinske kliničke prakse. Kao istraživački alati korišćeni su: upitnik o socio-demografskim karakteristikama ispitanika, Kratki oblik Mek-Gilovog upitnika o bolu (The McGill Pain Questionnaire, Short Form; SF-MPQ), Indeks težine nesanice (Insomina Severity Index-ISI) i Evropski upitnik za kvalitet života (European Quality of Life- EuroQol; EQ-5D). Rezutati su pokazali da se na kraju posete tretmanu, kod pacijenata obe studijske grupe pojavio se značajan odgovor na lečenje u skoro svim domenima istraživanja. Na kraju posete tretmanu, nivo bola ostaje nešto viši kod ispitanika eksperimentalne grupe, ali je statistička značajnost početne razlike nestala zbog većeg relativnog odgovora na terapiju u kontrolama (mereno procentom promene od početne vrednosti). Tretman je poboljšao kvalitet života pacijenata mnogo više kod eksperimentalnih nego kod kontrolnih subjekata, što je dokazano statistički značajnom razlikom u procentu promene u odnosu na početne vrednosti (približno 31% naspram približno 14). Saznanja o povezanosti bola i nesanice u praksi fizikalne medicine i rehabilitacije su od velikog značaja za kreiranje mera prevencije i adekvatne terapije za pacijenate sa bolnim sindromima.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".