Postoperative pain after cesarean section: assessment and management in a tertiary hospital in a low-income country
Bibliographic record
Abstract
BACKGROUND: There is little information about the current management of pain after obstetric surgery at Mulago hospital in Uganda, one of the largest hospitals in Africa with approximately 32,000 deliveries per year. The primary goal of this study was to assess the severity of post cesarean section pain. Secondary objectives were to identify analgesic medications used to control post cesarean section pain and resultant patient satisfaction. METHODS: We prospectively followed 333 women who underwent cesarean section under spinal anesthesia. Subjective assessment of the participants' pain was done using the Visual Analogue Scale (0 to 100) at 0, 6 and 24 h after surgery. Satisfaction with pain control was ascertained at 24 h after surgery using a 2-point scale (yes/no). Participants' charts were reviewed for records of analgesics administered. RESULTS: Pain control medications used in the first 24 h following cesarean section at this hospital included diclofenac only, pethidine only, tramadol only and multiple pain medications. There were mothers who did not receive any analgesic medication. The highest pain scores were reported at 6 h (median: 37; (IQR:37.5). 68% of participants reported they were satisfied with their pain control. CONCLUSION: Adequate management of post-cesarean section pain remains a challenge at Mulago hospital. Greater inter-professional collaboration, self-administered analgesia, scheduled prescription orders and increasing availability of analgesic drugs may contribute to improved treatment of postoperative pain with better pain scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".