Penerapan Metode Bayes untuk Mendiagnosa Penyakit Saraf Kejepit
Bibliographic record
Abstract
A pinched nerve is a condition where certain nerves are compressed by tissues around the body, such as bones, cartilage and muscles. This causes the nerve to become damaged with symptoms of severe pain, tingling, and numbness during activity. Nerve pain can spread throughout the body. For example, patients with radiculopathy type spinal cord disease make the patient numb, and the nerve pain can spread to the feet and hands. Sylvani General Hospital also provides expert doctors who treat various diseases, including pinched nerve disease suffered by patients. However, there are several problems that often occur to patients when going for direct consultation due to time constraints, long queues, long waits, long distances to the hospital, and lack of costs. Because agencies need to have a system that can manage existing symptom data on pinched nerve disease and make it an online expert substitute information by utilizing technological developments to get maximum diagnostic results, and patients can find out the initial symptoms of one of them numbness, leg pain, arm pain, back pain, muscle weakness in the type of pinched nerve disease, namely radiculopathy, carpal tunnel syndrome, pinched nerves in the waist, piriformis syndrome, radial tunnel syndrome and treatment first by consulting through a system that has been created using the Bayes method. From the calculation process using the Bayes method above, it is known that the diagnosis of pinched nerve disease is diagnosed with nerve root syndrome (Radiculopathy) (P01) with a percentage of 72.55%.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.012 |
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".