Determinants of magnesium sulphate use in women hospitalized at <29 weeks with severe or non-severe pre-eclampsia
Bibliographic record
Abstract
OBJECTIVE: Magnesium sulphate is recommended by international guidelines to prevent eclampsia among women with pre-eclampsia, especially when it is severe, but fewer than 70% of such women receive magnesium sulphate. We aimed to identify variables that prompt Canadian physicians to administer magnesium sulphate to women with pre-eclampsia. METHODS: Data were used from the Canadian Perinatal Network (2005-11) of women hospitalized at <29 weeks' who were thought to be at high risk of delivery due to pre-eclampsia (using broad Canadian definition). Unadjusted analyses of relative risks were estimated directly and population attributable risk percent (PAR%) calculated to identify variables associated with magnesium sulphate use. A multivariable model was created and a generalized estimating equation was used to estimate the adjusted RR that explained magnesium sulphate use in pre-eclampsia. The adjusted PAR% was estimated by bootstrapping. RESULTS: Of 631 women with pre-eclampsia, 174 (30.1%) had severe pre-eclampsia, of whom 131 (75.3%) received magnesium sulphate. 457 (69.9%) women had non-severe pre-eclamspia, of whom 291 (63.7%) received magnesium sulphate. Use of magnesium sulphate among women with pre-eclampsia could be attributed to the following clinical factors (PAR%): delivery for 'adverse conditions' (48.7%), severe hypertension (21.9%), receipt of antenatal corticosteroids (20.0%), maternal transport prior to delivery (9.9%), heavy proteinuria (7.8%), and interventionist care (3.4%). CONCLUSIONS: Clinicians are more likely to administer magnesium sulphate for eclampsia prophylaxis in the presence of more severe maternal clinical features, in addition to concomitant antenatal corticosteroid administration, and shorter admission to delivery periods related to transport from another institution or plans for interventionist care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".