HUBUNGAN KADAR SERUM NERVE GROWTH FACTORDENGAN KEJADIAN NEUROPATI PERIFER PADA PASIENKEMOTERAPI
Bibliographic record
Abstract
ABSTRAK Latar Belakang: Seiring perkembangan penggunaan kemoterapi pada terapi kanker, menimbulkan efek samping pada 30-60% pasien yang biasa dikenal dengan Chemotherapy-Induced Peripheral Neuropathy (CIPN). Penyakit ini dapat mempengaruhi kadar Nerve Growth Factor (NGF), yang merupakan suatu neurotropin yang berperan dalam pemeliharaan dan morfologi neuron. Mengetahui konsentrasi NGF pada pasien yang menjalani kemoterapi, dapat menjadi faktor dalam menentukan ringan atau beratnya CIPN dan prognosis terapi pada pasien. Metode: Penelitian observasional dengan desain cross-sectional dilakukan selama lima bulan yang dimulai dari bulan Juni sampai Oktober 2024. Kami mengambil tiga cc darah vena untuk pemeriksaan NGF serum dari semua pasien kanker yang menjalani kemoterapi secara konsekutif sampling. Kejadian neuropati diperiksa menggunakan skor Toronto Clinical Scoring System (TCSS) dengan uji statistik Mann Whitney digunakan untuk melihat hubungan NGF dan CIPN. Hasil: Diantara 60 pasien pada penelitian ini, didapatkan 17 pasien tidak mengalami CIPN dengan median kadar NGF sebesar 148.91 (82.42 – 470.50). Sedangkan 43 pasien yang mengalami CIPN memiliki median kadar NGF sebesar 103.26 (23.86-630.80). Hasil analisis statistik memperlihatkan bahwa terdapat hubungan yang bermakna antara kadar serum NGF dengan kejadian CIPN dengan nilai kemaknaan (p=0,029). Kesimpulan: Penelitian ini menunjukan bahwa pasien yang mengalami CIPN memiliki kadar NGF yang bermakna lebih rendah.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".