Characteristics of postpartum pain associated with vaginal and caesarean births.
Bibliographic record
Abstract
BACKGROUND: It is desirable that nursing mothers should return to normal activities so as to encourage early care of the newborn. This desire will be met if the common postpartum maternal morbidities are identified and managed accordingly. OBJECTIVE: To identify and characterize postpartum pain in the immediate postpartum period after vaginal or caesarean birth. METHODS: Women who delivered in our hospital; over 18 years, delivered of a live neonate and were in hospital for at least 2 days were studied. The socio-demographic characteristics, site of pain and self report of pain were recorded. The mothers were interviewed to describe the nature of postpartum pain using the Short-Form McGill Pain Questionnaire. RESULTS: A total of 116 patients participated in the study; 50/116 were nulliparous, and 100/116 received prenatal care. The incidence of pain was 82.8%. 55/74 women who had vaginal delivery and 41/42 post c-section women had pain. Only 51/96 women reported their pain and a good proportion of the women received some form of analgesia for the pain. Post c-section women were more likely to use affective descriptors than those who had vaginal delivery. The use of affective descriptors provoked higher VAS scores. CONCLUSION: Pain in the immediate postpartum period is common and more severe in women who had caesarean section than vaginal delivery. There is need to improve current methods of managing postpartum pain in the sub-region.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".