Fenugreek seed poultice versus cold cabbage leaves compresses for relieving breast engorgement: An interventional comparative study
Bibliographic record
Abstract
Background: Breast engorgement is an uncomfortable and painful condition affecting a large slid of mothers in their early postpartum period. Several approaches have been explored for pharmacological or non-pharmacological interventions applied to the treatment of breast engorgement. Some of the non-medical interventions include Fenugreek seed poultice and cold cabbage leaves compresses. Aim: Study the impact of nursing intervention on relieves of breast engorgement among puerperal breastfeeding women and compare Fenugreek seed poultice versus could cabbage leaves compresses as two different nursing care approaches of on relieving of breast-engorgement.Methods: Setting: Postnatal unit and outpatient clinic of Beni-Suef and El-Fayoum University Hospital. Design: A quasi-experimental comparative study. Subjects: A purposive sample of a total of 100 puerperal mothers; 50 in the Fenugreek group \& 50 in the cold Cabbage group. Tools: A specialized designed structured interview schedule and Breast Engorgement Assessment Scale (Numerical rating scale, Modified Reeda Scale, Six-points engorgement scale, Fever Chart, and LATCH breastfeeding charting scale).Results: A significant improvement of breast condition after intervention for both groups regardless of the applied measure was found; however, the improvement was better and shorter time among Fenugreek group than Cabbage group (p < .05). Conclusions: For the management of breast engorgement, both Fenugreek seed poultice and cold Cabbage leaves were effective. However, Fenugreek seed was more highly effective where breast engorgement was alleviated in a shorter time than cold Cabbage leaves. Recommendations: Further randomized controlled trials with possible placebo treatment should be carried out to elucidate the non-specific effects of Fenugreek seed poultice and cold Cabbage leaves application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".