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Record W3164995153 · doi:10.1071/ma21019

Targeting host-microbial interactions to develop otitis media therapies

2021· article· en· W3164995153 on OpenAlex
Lea‐Ann S. Kirkham, Ruth B. Thornton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicrobiology Australia · 2021
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsOtitisMedicineIntensive care medicineAntibioticsMicrobiomeClinical trialBioinformaticsBiologySurgeryInternal medicineMicrobiology

Abstract

fetched live from OpenAlex

Otitis media (OM; middle ear infection) is the most common reason for pre-school children to visit a doctor, be prescribed antimicrobials, or undergo surgery. Recent Cochrane reviews of clinical trials have identified that antibiotics and grommet surgery are only moderately effective in treating OM, with recurrent or persistent infection observed in one-third of children. Research efforts are focusing on developing improved therapies to treat OM and prevent disease recurrence. The recurrent nature of OM is mostly due to the persistence of bacterial pathogens within established biofilm in the middle ear. Promising novel therapies are harnessing host-microbe interactions to disrupt middle ear biofilm and permit antibiotics to work more effectively. New approaches are also being developed to prevent OM, including new vaccines and mining the host respiratory microbiome to develop novel bacterial therapies. This review describes how our improved knowledge of human and microbial interactions is driving development of OM therapies to improve health outcomes for children in Australia and worldwide.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0070.002

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 head0.037
GPT teacher head0.301
Teacher spread0.264 · 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