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Record W4415412461 · doi:10.1111/1471-0528.70054

Author Reply

2025· letter· en· W4415412461 on OpenAlexaff
Yasser Sabr, Sarka Lisonkova, Amélie Boutin, Chantal Mayer, K.S. Joseph

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2025
Typeletter
Languageen
FieldMedicine
TopicMaternal and fetal healthcare
Canadian institutionsUniversité LavalUniversity of British ColumbiaChildren's & Women's Health Centre of British Columbia
Fundersnot available
KeywordsMaternal deathDeath certificateDismissalCause of deathConfidentialityExtant taxonPleaAudit

Abstract

fetched live from OpenAlex

Dr. Deneux-Tharaux [1] criticises our use of death certificate data for illustrating the merits of classification systems based on multiple causes of maternal death [2]. As acknowledged in our study and elsewhere [2], death certificates underestimate maternal death rates and are less accurate with regard to cause-of-death information (compared with expert medical reviews). However, we were dismayed by Dr. Deneux-Tharaux's blanket (and somewhat solipsistic) dismissal of a less-than-ideal system for maternal death ascertainment that remains extant in many high-income countries. The objective of our study was to examine differences between two cause-of-death classification schemes, and we did this using a convenient dataset. We are not aware of evidence suggesting that cause-of-death inaccuracies in death certificates differ in magnitude for different leading obstetric causes of death, which would be necessary for invalidating the results of our study. Our study was motivated by a conundrum presented by the UK Confidential Enquiry into Maternal Deaths and the Dutch Audit Committee for Maternal Mortality and Morbidity [2]. If a woman with preeclampsia dies of an ensuing haemorrhage, how does one reconcile the underlying cause of death assigned in the Netherlands (viz. preeclampsia) with the underlying cause of death assigned in the United Kingdom (viz. haemorrhage)? These august committees concluded their discussion with a plea for suggestions to help resolve the impasse. This discussion may benefit further from reference to two issues related to the attribution of effects based on a singular underlying cause versus multiple causes. The World Health Organization (WHO), which equates severe maternal morbidity (SMM) with ‘near miss’, recommends that the ‘same classification of underlying causes [be] used for both maternal deaths and near misses’ [3]. Nevertheless, most SMM studies in the literature ignore this long-standing WHO injunction [4]. SMM rates and SMM component rates are estimated with women being assigned one or more severe maternal illnesses, rather than a singular underlying illness; studies show an exponential increase in case fatality among women with 1, 2, 3 or more severe maternal illnesses [4]. The second issue relates to the performance of cause-of-death classification systems, given temporal changes in causes of death. Increases in advanced maternal age and obesity have resulted in a rise in maternal multi-morbidity, and in the proportion of chronic disease-associated maternal deaths [5]. If a pregnant woman with pre-existing hypertension, diabetes or other chronic diseases develops a fatal case of severe preeclampsia, embolism or stroke, the underlying cause of death assigned would likely be one of the latter severe pregnancy complications. However, a focus on the singular underlying cause of death would obscure the rising rate of chronic disease-associated maternal deaths, irrespective of whether the cause-of-death information was obtained from death certificates or expert review. On the other hand, a multiple cause-of-death analysis would show the rise in chronic disease-associated maternal deaths [5]. In conclusion, we reiterate that cause-of-death assignment based on multiple causes of death is consistent with contemporary multi-factorial models of causation and will improve the effectiveness of clinical and public health programs aimed at maternal death prevention. All authors contributed to the conception, drafting and revision of this letter. All authors approved the final version for submission. The authors declare no conflicts of interest. There is no data presented in this Letter to the Editor (response).

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)0.004
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.036
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0360.030
Insufficient payload (model declined to judge)0.0170.015

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 head0.041
GPT teacher head0.358
Teacher spread0.317 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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