HUBUNGAN INDEKS MASSA TUBUH (IMT) DENGAN RESIKO KAKI DIABETIK PADA PASIEN DIABETES MELITUS TIPE 2
Bibliographic record
Abstract
Meningkatnya kejadian diabetes melitus mengakibatkan pula meningkatnya komplikasi, salah satunya kaki diabetik. Kaki diabetik dapat disebabkan oleh berbagai faktor resiko diantaranya adalah faktor kegemukan yang ditandai dengan tingginya indeks massa tubuh (IMT). Penelitian ini bertujuan untuk mengetahui hubungan antara hubungan indeks massa tubuh (IMT) dengan resiko kaki diabetik pada pasien diabetes melitus tipe 2. Penelitian ini menggunakan rancangan analitik korelasional dengan pendekatan cross sectional. Responden berjumlah 68 orang yang diambil secara purposive sampling. Indeks massa tubuh diperoeh dari pengukuran berat badan dan tinggi badan yang dihitung melalui rumus BB/TB2. Sedangkan resiko kaki diabetik diambil melalui pemeriksaan skrining resiko kaki diabetik dengan Screening Tools Inlow’s 60 second diabetic foot dari Canadian Association of Wound Care. Hasil penelitian menunjukkan bahwa sebagian besar responden memiliki IMT ≥ 23 (73,5%) dan beresiko rendah terjadinya kaki diabetik (67,6%). Analisis statistik dengan uji Chi square diperoleh tidak ada hubungan antara indeks massa tubuh (IMT) dengan resiko kaki diabetik (p value 0,245). Meskipun beresiko rendah untuk mengalami kaki diabetik, namun terdapat beberapa faktor resiko yang dimiliki oleh pasien diantaranya penggunaan alas kaki yang salah, adanya kesemutan dan neuropati. Sehingga perlu dilakukan pengendalian kadar gula darah melalui pengontrolan berat badan dan perawatan kaki
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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".