ORTOPEDi VE TRAVMATOLOJi HASTALARINDA POSTOPERATiF AĞRI TANILAMASI
Bibliographic record
Abstract
Ara ttrma, Ortopedi ve Travmatoloji hastalannda, postoperatif dOnemde agn tan1lamas1 yapmak amactyla planland1. Bu tan1mlay1c1 c;ah man1n Otneklemini; tedavi amac1yla i.iniversite hastanesine ba vuran, btiyi.ik ameliyat gec;iren, postoperatif dOnemde hastanede yat1 1n1n 3. gtinUnde, 18 ya ve Usttinde olan, genel anestezi alan, agnya neden olan ba$ka bir akut hastahg1 bulunmayan, ameliyat s!fas1nda ve sonras1 3 gtin i<;inde hi<;bir komplikasyon geli meyen bireyler aras1ndan olas1hks1z Orneklerne yOntemi ile sec;ilen 150 ki$i olu turdu. Veriler Hasta Bilgi Formu vc McGill Melzack Agn Soru Formu (MASF) arac1hg1 ile topland1. <;:ahimada, 56-65 yai grubundaki hastalann agn $iddetinin, 18-25 y grubundaki hastalara gOre daha ytiksek, kad1n hastalann agn ifadclerinin, crkek hastalara gore daha yeterli ve agn iddetinin, erkek hastalara gore daha ytiksek, ya$amlann1 en c;ok Karadeniz Bolgesi' nde gei;irmi hastalann agn $iddetinin, ic; Anadolu Bolgesi' nde ya am1 hastalara gore daha ytiksek, vertebra rckonstrtiksiyonu ameliyatt olan hastalann agn iddetinin, diz protezi ameliyat1 gec;iren hastalara gore daha ytiksek oldugu gOrtildti ve istatistiksel ac;1dan anlamh bir fark oldugu saptand1. ah$ma bulgulan, postoperatif donemde agn tan1lamas1 yapman1n tinemini gOstermi tir.Anahtar Kelimeler: Postoperatif agn, Postoperatif agn tan1lamas1.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".