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Record W2133969445 · doi:10.1017/mdh.2012.79

Mineral Waters, Electricity, and Hemlock: Devising Therapeutics for Children in Eighteenth-Century Institutions

2013· article· en· W2133969445 on OpenAlexaff
Ashley Mathisen

Bibliographic record

VenueMedical History · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsUniversity of Guelph
FundersWellcome Trust
KeywordsDispensaryMedical practiceVariety (cybernetics)MedicineMedical educationFamily medicineComputer science

Abstract

fetched live from OpenAlex

The development of paediatric medicine as a formal field of medical specialisation is usually traced to the mid-nineteenth century at the earliest. While it is true that formal specialisation in children's medicine was not, on the whole, typical for eighteenth-century medical practitioners, many displayed a deep and lasting interest in the diseases of children, and were consequently eager to develop therapeutic practices which could be targeted at infants and children. This led to a variety of attempts at innovation, many of which benefitted from the co-operation of, and opportunities afforded by, institutions. By examining the efforts of several medical practitioners at the London Foundling Hospital and at the Dispensary for the Infant Poor, this article explores how eighteenth-century medical practitioners used their affiliations with institutions to address the problems of devising or adapting therapeutic practices and treatments for children. In tailoring medical practice to suit children and, more specifically, in using institutions to do so, medical practitioners were demonstrating that child patients required special consideration, that children's diseases could be managed medically and with the benefit of new approaches and methods, and that children's health, as a whole, was the province of medical practitioners.

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.036
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.241
Teacher spread0.194 · 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 designQualitative
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

Citations20
Published2013
Admission routes1
Has abstractyes

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