Rasio Neutrofil Limfosit Sebagai Parameter Prognosis Kanker Penis : Sebuah Tinjauan Sistematis Dan Meta-Analisis
Bibliographic record
Abstract
Kanker penis adalah keganasan yang relatif jarang terjadi. Berbagai biomarker digunakan untuk memprediksi prognosis kanker, salah satunya adalah rasio neutrofil terhadap limfosit. Tinjauan sistematis dan meta-analisis ini untuk mengevaluasi nilai prognostik NLR pada kanker penis. Pencarian sistematis dilakukan dengan menggunakan pedoman PRSMA pada beberapa database Scopus, Science-direct, dan PubMed. Hasil uatma adalah metastass kelenjar getah bening, cancer specific survival, dan overall survival. Analisiis bias terhadap studi observasional dilakukan dengan menggunakan Newcastle Ottawa Scale (NOS). Sebanyak enam studi retrospektif dimasukkan dalam analisis. Nilai cut-off dari studi yang disertakan NLR berkisar antara 2,6 hingga 3,59. Meta-analisis menunjukkan bahwa pasien kanker penis dengan NLR tinggi memiliki metastasis kelenjar getah bening inguinal yang lebih tinggi serta OS, dan CSS yang lebih rendah pada analisis univariat (masing-masing, OR 3,56, 95% CI 2,38, 5,32, pp < 0,01; HR 1,69, 95% CI 0,95, 3, p = 0,07; HR 4,19, 95% CI 2,19, 8,01, p < 0,0001). Selanjutnya, meta-analisis mengungkapkan bahwa NLR adalah prediktor independen metastasis kelenjar getah benning innguinal dan CSS (masing-masing OR 6,67, 95% CI 2,44, 18,22, p <0,01; HR 2,15, 95% CI 1,23, 3,73, p p <0,01). Sebagai kesimpulan NLR adalah prediktor independen LNM, CSS, serta prediktor OS pada kanker penis.
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 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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".