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Record W2184173245 · doi:10.1136/bmj.h6520

Lyme disease: time for a new approach?

2015· editorial· en· W2184173245 on OpenAlexaff
Liesbeth Borgermans, Christian Perronne, Ran D. Balicer, Ozren Polašek, Valérie Obsomer

Bibliographic record

VenueBMJ · 2015
Typeeditorial
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsLyme diseaseComputer scienceVirologyMedicine

Abstract

fetched live from OpenAlex

Many more questions than answers Lyme disease is the most common vector borne disease in North America and Europe, with 300 000 new cases in the United States1 and an estimated 100 000 new cases in Europe each year.2 These numbers are likely to be underestimates because case reporting is inconsistent3 and many infections go undiagnosed.4 Climate change may have contributed to a rapid increase in tick borne diseases, with migratory birds disseminating infected ticks.5 Our common understanding of Lyme disease is that a tick bite is followed by the development of a classic rash pattern (erythema migrans). When treated early with a relatively short course of antibiotics, most patients have good outcomes.6 But the standard two tier testing for Lyme disease is inaccurate in the early …

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)0.011
metaresearch head score (Gemma)0.041
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.041
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0060.012
Open science0.0030.002
Research integrity0.0130.030
Insufficient payload (model declined to judge)0.0190.015

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 head0.017
GPT teacher head0.285
Teacher spread0.268 · 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
GenreEditorial

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

Citations9
Published2015
Admission routes1
Has abstractyes

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