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Record W2889636055

Primena algometrije kod osoba sa cervikalnom i lumbalnom radikulopatijom

2018· dissertation· sr· W2889636055 on OpenAlexaboutno aff
Nikola Vučinić

Bibliographic record

VenueNational Repository of Dissertations in Serbia · 2018
Typedissertation
Languagesr
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine
DOInot available

Abstract

fetched live from OpenAlex

Uvod: Radikulopatija je obično praćena bolovima i drugim senzornim i motornim poremećajima, uz smanjenje kvaliteta života u različitom obimu. Algometrija kao visokosenzitivna metoda pruža objektivan uvid u stepen bola, dok se upotrebom upitnika na jednostavan način mogu proceniti karakteristike bola i biopsihosocijalni status pacijenta. Cilj: Istraživanje je sprovedeno kako bi se izmerili prag bola i prag tolerancije na bol kod pacijenata sa cervikalnom i lumbalnom radikulopatijom i utvrdila moguća povezanost bola sa biopsihosocijalnim faktorima. Materijal i metode: Studijom je pre započinjanja i posle završavanja terapijskog ciklusa ispitano 60 pacijenata sa dijagnostikovanom cervikalnom radikulopatijom (30 muškaraca i 30 žena) i 60 pacijenata sa dijagnostikovanom lumbalnom radikulopatijom (30 muškaraca i 30 žena). Svi pacijenti su bili hospitalno lečeni u okviru Klinike za medicinsku rehabilitaciju, Kliničkog centra Vojvodine u Novom Sadu, a terapijski ciklus je u proseku trajao 14-21 dan. U istraživanju su korišćenitest za detekciju bola (Pain Detect Test), kratki upitnik o bolu (Brief Pain Inventory), indeks onesposobljenosti zbog bolova u vratu (Neck Disability Index), Kvebekova skala onesposobljenosti kod lumbalnog sindroma (Quebec Back Pain Disability Scale), bolnička skala za anksioznost i depresiju (Hospital Anxiety and Depression Scale), upitnik za procenu prisustva straha od fizičke aktivnosti/posla i njihovog izbegavanja (The Fear-Avoidance Beliefs Questionnaire) i skala katastrofizma bola (Pain Catastrophizing Scale). Rezultati: Nije uočena statistički značajna razlika algometrijskih vrednosti između pacijenata sa cervikalnom radikulopatijom i pacijenata sa lumbalnom radikulopatijom. Ustanovljeno je da osobe ženskog pola imaju niži prag bola i nižu toleranciju na bol od osoba muškog pola. Poređenjem algometrijskih vrednosti pre započinjanja i posle završavanja terapijskog ciklusa zapaža se da je program rehabilitacije povoljno uticao na pacijente sa lumbalnom radikulopatijom, dok je kod pacijenata sa cervikalnom radikulopatijom došlo do pogoršanja tegoba. Prisutna je negativna korelacija između vrednosti izmerenih algometrom i skorova za anksioznost, depresiju i strah od fizičke aktivnost i posla, što znači da biopsihosocijalni faktori u velikoj meri utiču na bolnost. Zaključci: Kvantifikovanje i mapiranje bola uz pomoć algometra i utvrđivanje biopsihosocijalnog statusa putem upitnika će omogućiti primenu adekvatne terapije kod pacijenata, koja je zasnovana na individualnom pristupu. Istovremeno bi primenjenom metodologijom bila postignuta bolja verifikacija rezultata rehabilitacionog programa.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.003

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 head0.011
GPT teacher head0.300
Teacher spread0.289 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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