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Record W7099208747

Historical background

2015· article· en· W7099208747 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsTuberculosisDiseaseHistory of tuberculosisGreeksPrehistoryQuarter (Canadian coin)Wasting
DOInot available

Abstract

fetched live from OpenAlex

Tuberculosis has afflicted man since prehistoric times. Evidence of spinal tuberculosis has been found in neolithic skeletons as well as in early Egyptian remains. The ancient Greeks recognised tuberculosis and called it pthisis to characterise the wasting that occurs in the disease. Tuberculosis was not a major health problem until the Industrial Revolution when crowded urban communities were created thus facilitating the spread of the infection. In Europe, during the seventeenth and eighteenth centuries, as many as a quarter of all deaths could be attributed to tuberculosis. In 1865 Villemin demonstrated the infective nature of the disease when he successfully transmitted the disease to guinea pigs by inoculating them wi.th diseased tissues but it was not until 1882 that Koch discovered the aetiological agent and elucidated the pathogenesis of the disease. With better living conditions and the advent of modem chemotherapy, the incidence of tuberculosis in many developed countries has, since the turn of the century, decreased dramatically. In many low income, developing countries however, there has been no observable decline in incidence. The world situation The current world-wide situation with tuberculosis gives rise to concern. The WorId Health Organisation estimates that 8 million new cases of tuberculosis occurred in 1990. In the same year tuberculosis

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.885
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1150.036

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 head0.183
GPT teacher head0.396
Teacher spread0.213 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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