Audit of use of the partograph at the University College Hospital, Ibadan.
Bibliographic record
Abstract
Study assessed documentation on the partograph and its influence on decision-making at the University College Hospital (UCH), Ibadan. Partograph records of parturient during 2004 were retrospectively reviewed. Four hundred and forty-five women had partographic monitoring. High-risk patients were more likely to receive closer (quarter-hourly) monitoring than low-risk women (chi2 = 45.7, p < 0.0001). Documentation was high and not influenced by woman's risk or booking status. Descent of presenting part and liquor status were the least recorded parameters. When tracing crossed the alert line (31.2%) or reached the action line (10.1%), augmentation of labour was more often (but not statistically significant) resorted to than emergency Caesarean section. When tracing crossed the action line however, intervention was significantly more likely to be emergency Caesarean section than augmentation of labour (88.2% vs. 11.8%), chi2 = 5.3, p < 0.05. Intervention for inadequate uterine contractions would more likely be augmentation of labour than emergency Caesarean section (81.4% vs. 18.6%), chi2 = 3.9, p < 0.05. This decision was not significantly influenced by the risk status (chi2 = 0.003, p > 0.05). Outcome of labour was favourable for majority of low and high-risk women and their infants. The partograph is universally employed in monitoring of labour at UCH Ibadan. Its use significantly influences decision-making and associated with positive labour outcome among low/high-risk parturient. It is recommended as the sine qua non tool for intra-partum monitoring in all health facilities in Nigeria to reduce maternal complications.
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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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".