Diagnosis of Baby Blues Syndrome sing the Certainty Factor Method (Case Study: FULL BETHESDA Hospital)
Bibliographic record
Abstract
Baby blues syndrome is a psychological disorder experienced by women after giving birth, such as feeling excessively upset and sad, and tired for no apparent reason. About 80% of women who have just given birth will experience Baby blues Syndrome, if this continues and is prolonged it will be very dangerous for the health of the mother and baby. From the problems above, the hospital needs to have an additional system that can help make it easier for the medical team to speed up handlers in analyzing and diagnosing Baby blues Syndrome suffered by patients using the certainty factor method. The purpose of this research is to build an expert system for diagnosing the symptoms of Baby blues syndrome using the certainty factor method. Based on the results of the CF calculation, the highest score is the type of baby blues syndrome with a value of 0.9602 or 96.02%. From the results obtained, the system identified that the patient had a type of baby blues syndrome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".