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Record W3138920986 · doi:10.3390/microorganisms9030663

The Impact of Tick-Borne Diseases on the Bone

2021· review· en· W3138920986 on OpenAlexaff
Imran Farooq, Tara J. Moriarty

Bibliographic record

VenueMicroorganisms · 2021
Typereview
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEhrlichiosisTickAnaplasmosisDiseaseTick-borne diseaseLyme diseaseMedicineEncephalitisBiologyBabesiosisVirologyImmunologyVirusPathology

Abstract

fetched live from OpenAlex

Tick-borne infectious diseases can affect many tissues and organs including bone, one of the most multifunctional structures in the human body. There is a scarcity of data regarding the impact of tick-borne pathogens on bone. The aim of this review was to survey existing research literature on this topic. The search was performed using PubMed and Google Scholar search engines. From our search, we were able to find evidence of eight tick-borne diseases (Anaplasmosis, Ehrlichiosis, Babesiosis, Lyme disease, Bourbon virus disease, Colorado tick fever disease, Tick-borne encephalitis, and Crimean-Congo hemorrhagic fever) affecting the bone. Pathological bone effects most commonly associated with tick-borne infections were disruption of bone marrow function and bone loss. Most research to date on the effects of tick-borne pathogen infections on bone has been quite preliminary. Further investigation of this topic is warranted.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.897
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.369
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2021
Admission routes1
Has abstractyes

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