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Record W2118741833 · doi:10.1016/j.preghy.2012.04.033

OS032. Pharmacotherapy for pre-eclampsia in low and middle income countries: An analysis of essential medicines lists (EMLS).

2012· article· en· W2118741833 on OpenAlexaff
S. Lalani, Tabassum Firoz, Laura A. Magee, Rohan G. T. Lowe, Diane Sawchuck, Beth A. Payne, R. Gordon, Marianne Vidler, Peter von Dadelszen

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer Research and Treatment
Canadian institutionsUniversity of British ColumbiaChildren’s Health Research InstituteChild and Family Research InstituteChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsEclampsiaMedicinePregnancyObstetrics

Abstract

fetched live from OpenAlex

INTRODUCTION: Pre-eclampsia is the second leading cause of maternal mortality in low and middle income countries (LMIC). Pharmacological management of pre-eclampsia has five major components including antihypertensive therapy for severe and non-severe hypertension, magnesium sulphate for prevention or treatment of eclampsia, treatment of pre-eclampsia-related end-organ complications, antenatal corticosteroids for acceleration of fetal pulmonary maturity given iatrogenic preterm delivery for maternal and/or fetal indications, and labour induction for such indicated deliveries. Essential medicines are defined by the World Health Organization (WHO) as "drugs that satisfy the health care needs of the majority of the population". Essential Medicines Lists (EMLs) detail these essential medicines within an individual country and support the argument that the medication should be routinely available. OBJECTIVES: To determine how many drugs required for comprehensive pre-eclampsia management are listed in national EMLs of LMIC. METHODS: We conducted a descriptive analysis of relevant drug prevalence on identified EMLs. We searched for the national EMLs of the 144 LMIC identified by the World Bank. EMLs were collected by broad based internet searches and in collaboration with the WHO. The EMLs were surveyed for therapies for the different aspects of pre-eclampsia management: hypertension (non-severe and severe with oral or parenteral agents), eclampsia, pre-eclampsia complications (e.g., pulmonary oedema, thrombosis), preterm birth, and labour induction. RESULTS: EMLs were located and reviewed for 58(40.3%) of LMIC. One or more parenteral antihypertensive agents were listed in 51(87.9%) EMLs. The most common agents were: hydralazine (67.2%), verapamil (58.6%), propranolol (39.7%) and sodium nitroprusside (37.9%); parenteral labetalol was listed by only 19.0% of EMLs. The most prevalent oral antihypertensive therapies listed were: nifedipine (96.6%, usually 10 or 20mg intermediate-acting tablets), methyldopa (94.8%), propranolol (89.7%), and atenolol (87.9%). Captopril, enalapril, hydrochlorothiazide and spironolactone were commonly listed. Magnesium sulphate for prevention and management of eclampsia was present in 86.2% of EMLs (and its antidote, calcium gluconate in 82.8%). To manage complications of pre-eclampsia, oral frusemide was listed in 94.8% of EMLs and parenteral heparin in 91.4%. Most EMLs listed parenteral dexamethasone (91.4%) for acceleration of fetal pulmonary maturity and oxytocin (98.3%) or a prostanoid (usually misoprostol, 39.7%) for labour induction. CONCLUSION: EMLs of LMIC provide comprehensive coverage of all aspects of recommended pre-eclampsia pharmacotherapy. These EMLs may be used as advocacy tools to ensure the availability of these therapies within each country.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0070.019
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.027
GPT teacher head0.351
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2012
Admission routes1
Has abstractyes

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