PENGANGKAAN (KUANTIFIKASI) PERIKSAAN PULASAN GRAM DI BERBAGAI JENIS BAHAN PEMERIKSAAN
Bibliographic record
Abstract
In a clinical microbiology laboratory the Gram staining is used to classify bacteria on the basis of their forms, sizes, cellularmorphologies, and Gram reactions. Additionally it is a critical test for rapid presumptive diagnosis of infectious agents and serves toassess the quality of clinical specimens. Several methods of Gram staining quantification are already applied: Canadian Coalition forQuality in Laboratory Medicine (CCQLM), Clinical Microbiology Proficiency Testing (CMPT), and World Health Organization (WHO).Each method consists of several criteria for quantification and its interpretation, such as neutrophil cell (polymorphonuclear cells),squamous epithelial cell, and number of microorganisms. Those methods aren't limited in sputum specimen, but also could be used forother specimen such as urine, vaginal discharge, and other body fluids. These methods are also could be used as screening for specimenbefore it is continued into further testing. Even though there is several limitation for each method, quantification method of Gramstaining could be provide better diagnostic value in microbiology laboratory as an early detection in the examination to get betterdiagnosis as well as treatment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".