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Record W2792085262 · doi:10.1177/0967772017743068

Martha Wollstein: A pioneer American female clinician-scientist

2018· article· en· W2792085262 on OpenAlexaff
Jeanne E. Abrams, James R. Wright

Bibliographic record

VenueJournal of Medical Biography · 2018
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsCalgary Laboratory ServicesAlberta Children's HospitalUniversity of Calgary
FundersRockefeller University
KeywordsSpecialtyEthnic groupFamily medicineMedicineGerontologyMedical educationPediatricsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Martha Wollstein was not only the first fully specialized pediatric perinatal pathologist practicing exclusively in a North America children's hospital, she also blazed another pathway as a very early pioneer female clinician-scientist. Wollstein provided patient care at Babies Hospital of New York City from 1891 until her retirement in 1935, and also simultaneously worked for many years as a basic scientist at the prestigious Rockefeller Institute for Medical Research. Wollstein published over 65 papers, many frequently cited, during her career on a wide range of topics including pediatric and infectious diseases. Wollstein was a rare female in the field of pathology in an era when just a relatively small number of women became doctors in any medical specialty. Wollstein was born into an affluent Jewish American family in New York City in 1868 and graduated from the Women's Medical College in 1889. This paper explores her family support and ethnic and religious background, which helped facilitate her professional success. During her time, she was recognized internationally for her research and was respected for her medical and scientific skills; unfortunately today her important career has been largely forgotten.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0120.006

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 head0.040
GPT teacher head0.447
Teacher spread0.407 · 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
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

Citations6
Published2018
Admission routes1
Has abstractyes

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