Promjena zagriza i opseg otvaranja usne šupljine prije i poslije fizioterapijskog programa
Bibliographic record
Abstract
Uvod: Poremećaji temporomandibularnog zgloba (TMP) obuhvaćaju niz stanja koja utječu na anatomske i funkcionalne karakteristike temporomandibularnog zgloba (TMZ). Najčešće se manifestiraju hipermobilnošću zgloba, miofascijalnom boli u žvačnim i vratnim mišićima, poremećajima zglobnog diska poput klikanja te poremećajima koordinacije mandibularnih pokreta. Liječenje TMP-a započinje konzervativnim pristupom, dok se operativno liječenje razmatra samo u određenim slučajevima.Materijali i metode: Studija je provedena u privatnoj praksi u Münchenu, Njemačka. Ispitanici su prije i nakon terapije ispunili standardizirani upitnik o poremećajima temporomandibularnog zgloba (TMJ/TMD Questionnaire). Program fizioterapije uključivao je medicinsku masažu i kineziterapiju u trajanju od 3 tjedna, s ukupno 6 tretmana.Rezultati: U istraživanju je sudjelovao 31 ispitanik stariji od 18 godina. Potpuno i snažno otvaranje usta bilo je moguće kod 87,1% ispitanika, dok je djelomično bilo moguće kod 12,9%. Promjene u zagrizu i učestalost tegoba značajno su se smanjile nakon fizioterapije, dok je amplituda pokreta kod manjeg broja ispitanika ostala nepromijenjena.Zaključci: Program fizioterapije pokazao se učinkovitim u smanjenju učestalosti problema i poboljšanju promjena zagriza kod osoba s poremećajima temporomandibularnog zgloba. Za dodatno poboljšanje opsega pokreta preporučuje se produženje terapije i uključivanje dodatnih terapijskih modaliteta.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".