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Record W2321267965 · doi:10.1177/0967772015626013

Benjamin Terry and his rapid razor section method for intraoperative diagnosis

2016· article· en· W2321267965 on OpenAlexaff
Stéphan R. Duchesne, Christopher Naugler, James R. Wright

Bibliographic record

VenueJournal of Medical Biography · 2016
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsBattleFrozen section procedureMedicineSection (typography)StainMedical practiceGeneral surgerySurgeryPathologyHistoryMedical educationAncient historyStainingComputer science

Abstract

fetched live from OpenAlex

Benjamin Taylor Terry (1876-1955), a little-known pathologist, played a critical role in the popularization of intraoperative diagnostic techniques in the 1920s and 1930s. He developed both a stain and his own rapid razor section method. Intraoperative diagnostic techniques were ultimately responsible for the transition of the practice of pathology and laboratory medicine from private commercial laboratories to a hospital-based practice, forever changing the history of pathology and surgery in North America. Although the intraoperative diagnostic technique he personally developed was reportedly better, faster and more economical than frozen sections, the latter ultimately won the battle for intraoperative diagnostic supremacy.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.343
Teacher spread0.322 · 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.

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

Citations3
Published2016
Admission routes1
Has abstractyes

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