PERBEDAAN KADAR MCV DAN MCHC PADA PASIEN KANKER SERVIKS DENGAN PERDARAHAN DAN TANPA PERDARAHAN
Bibliographic record
Abstract
: Perdarahan sering terjadi pada pasien dengan kanker serviks, dan dapat menimbulkan anemia. Tipe anemia yang diderita bisa ditentukan dengan parameter Mean Corpuscular Volume (MCV) dan Mean Corpuscular Hemoglobin Concentration (MCHC). Penelitian ini bertujuan untuk mengetahui perbedaan kadar MCV dan MCHC pada pasien kanker serviks dengan perdarahan dan tanpa perdarahan. Desain penelitian ini adalah cross-sectional. Subjek penelitian adalah pasien kanker serviks di RSUD Ulin Banjarmasin periode April sampai Agustus 2021 yang sesuai dengan kriteria inklusi dan eksklusi. Variabel bebasnya adalah perdarahan dan variabel terikatnya adalah kadar MCV dan MCHC. Analisis dilakukan dengan uji T tidak berpasangan dan uji Mann-Whitney. Diperoleh 45 orang pasien kanker serviks, dan hasil penelitian menunjukkan bahwa 58% mengalami perdarahan. Rerata kadar MCV pada pasien dengan dan tanpa perdarahan adalah 73,74±7,64 fl dan 81,26±4,98 fl (p < 0,001), dan rerata kadar MCHC pada pasien dengan dan tanpa perdarahan adalah 30,89±1,45 g/dl dan 32,41±1,47 g/dl (p < 0,001). Dapat disimpulkan bahwa terdapat perbedaan kadar MCV dan MCHC pada pasien kanker serviks dengan perdarahan dan tanpa perdarahan.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".