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Record W4383534765 · doi:10.17816/kmj109465

Issues of surgical treatment of tumors considered at the meetings of the Saratov Physico-Medical Society in the last quarter of the 19th century

2023· article· en· W4383534765 on OpenAlexaboutno aff
Elena N. Kurochkina, Arkady I. Zavyalov, А. С. Толстокоров, Evgeny Yu. Osincev, Yuri Kovalenko

Bibliographic record

VenueKazan medical journal · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)MedicineRehabilitationPsychological interventionFamily medicineGeneral surgeryHistoryNursingPhysical therapy

Abstract

fetched live from OpenAlex

The article highlights the scientific and practical contribution of the Physico-Medical Society to the formation and development of oncology and achievements in providing medical care to patients in the Saratov province at the end of the 19th century. The work was done on the basis of the minutes of the meetings and the proceedings of the society in 18761900 and annual reports of the provincial zemstvo and city hospitals, indicating not only an increase in the number of patients with neoplasms, but also an improvement in clinical diagnostic methods, an expansion of indications for surgical treatment and rehabilitation measures after surgical interventions. The most interesting clinical cases and rare localizations of neoplasms are presented, brief speeches of members of the society when discussing reports, are given, that may be of not only historical and medical, but also of great clinical interest to modern practitioners. Published materials show that the diagnosis and surgical treatment of neoplasms in patients at the end of the 19th century were carried out at a high professional level.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.266
Teacher spread0.237 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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