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Record W3049625634 · doi:10.1126/sciadv.abc1828

A novel mouse model of chronic suppurative otitis media and its use in preclinical antibiotic evaluation

2020· article· en· W3049625634 on OpenAlexafffund
Kelly Khomtchouk, Ali Kouhi, Anping Xia, L. Bekale, Solange Massa, Johanna M. Sweere, Daniel Pletzer, Robert E. W. Hancock, Paul L. Bollyky, Peter L. Santa Maria

Bibliographic record

VenueScience Advances · 2020
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity of British Columbia
FundersStanford Maternal and Child Health Research InstituteCanada Research ChairsAction on Hearing LossMichael Smith Health Research BCDivision of Loan RepaymentCanadian Institutes of Health ResearchStanford University
KeywordsAntibioticsChronic Suppurative Otitis MediaOtitisMedicineIntensive care medicineMicrobiologyBiologySurgery

Abstract

fetched live from OpenAlex

Chronic suppurative otitis media (CSOM) is a neglected pediatric disease affecting 330 million worldwide for which no new drugs have been introduced for over a decade. We developed a mouse model with utility in preclinical drug evaluation and antimicrobial discovery. Our model used immune-competent mice, tympanic membrane perforation and inoculation with luminescent Pseudomonas aeruginosa that enabled bacterial abundance tracking in real-time for 100 days. The resulting chronic infection exhibited hallmark features of clinical CSOM, including inhibition of tympanic membrane healing and purulent ear discharge. We evaluated the standard care fluoroquinolone ofloxacin and demonstrated that this therapy resulted in a temporary reduction of bacterial burden. These data are consistent with the clinical problem of persistent infection in CSOM and the need for therapeutic outcome measures that assess eradication post-therapeutic endpoint. We conclude that this novel mouse model of CSOM has value in investigating new potential therapies.

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.131
GPT teacher head0.383
Teacher spread0.252 · 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 designBench or experimental
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

Citations30
Published2020
Admission routes2
Has abstractyes

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