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Record W2047101827 · doi:10.1177/0141076813507843

How dramatic were the effects of handwashing on maternal mortality observed by Ignaz Semmelweis?

2013· article· en· W2047101827 on OpenAlexaff
P. La Rochelle, Anne‐Sophie Julien

Bibliographic record

VenueJournal of the Royal Society of Medicine · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineDemographyStandardized mortality ratioMortality ratePediatricsSurgery

Abstract

fetched live from OpenAlex

Questions remain about how much evidence is needed to accept or at least to consider an observation as providing sufficiently strong evidence to justify a change in clinical practice,1,2 even when the key outcome measure is objective, definitive and clinically very important. Maternal death is one such outcome, and Ignaz Semmelweis’ observation of the reduction in maternal deaths associated with the introduction of handwashing in a maternity hospital in Vienna provides an illustrative example.3,4 A plot of the rate of maternal deaths every month from January 1841 until May 1847 shows substantial variability and some evidence of a pattern in the changes, possibly related to the seasons.5 After handwashing was introduced in June 1847, deaths were less frequent and showed less monthly variability in the period up to February 1849. How much analysis is needed to convince people that this is an effect of handwashing and not a chance association, or a reflection of bias?2 Eyeballing the plot shown in Figure 1, many observers might consider the reduction in deaths following the introduction of handwashing to be real and significant. When considering the high risk of bias in studies using historical controls, Glasziou et al.1 suggested considering an effect as likely to be real if a risk ratio of between 5 and 10 has been observed. In this case, a comparison of the average monthly mortality rate before the introduction of handwashing (10.65%, blue points) and after it (1.98%, red points) yields a risk ratio of 5.38. We can explore these data in more depth by analysing this interrupted time series with a Seasonal AutoRegressive Integrated Moving Average model. The monthly mortality rate can be formulated as follows: MRt=B×H+AR×MRt-1+MA×Et-12+Et where MR = mortality rate, t = month, B = handwashing coefficient, H = 0 (before the introduction of handwashing), H = 1 (after the introduction of handwashing), AR = first-order autoregressive coefficient, MA = monthly moving average coefficient and E = random error. Figure 1. Monthly mortality rate from puerperal fever. The estimations of the three coefficients of the model are presented in the table below. This yields an estimated absolute reduction in maternal mortality of 8.87%, which is highly statistically significant (P = 0.0042). The reduction is also of great human significance: for every 11 women examined after handwashing, one fewer died. The analysis could be extended to take into account the data from the two clinics concerned, and to cover an earlier period, during which postmortem examinations were not done routinely.6 This discovery happened before the germ theory take off with the work of Louis Pasteur in the early 1860s. The miasma theory was prevalent and no strong explanation was available to support this impressive observation. These different levels of analysis strongly support the existence of a beneficial impact of handwashing on the risk of maternal mortality. This is not to suggest that data of this level of confidence are sufficient to introduce changes in clinical practice, which is influenced by medical tradition and cultural and other factors.4,6 In Semmelweis’ case, the evidence of an important impact of handwashing on maternal mortality should at least have led traditional medical practices to have been questioned.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.272
Teacher spread0.236 · 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 teacher head, 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".

Quick stats

Citations13
Published2013
Admission routes1
Has abstractyes

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