Culture-Independent Molecular Analysis of Microbial Constituents of the Healthy Human Outer Ear
Bibliographic record
Abstract
Molecular-phylogenetic sequence analyses have provided a new perspective on microbial communities by allowing the detection and identification of constituent microorganisms in the absence of cultivation. In this study we used broad-specificity amplification of ribosomal DNA (rDNA) genes to survey organisms present in the human outer ear canal. Samples were obtained from 24 individuals, including members of three extended families, in order to survey the resident microbiota and to examine microbial population structures in individuals related by familial or household associations. To examine the stability of the microbial populations, one individual was sampled four times and another twice over a 14-month period. We found that a distinct set of microbial types was present in the majority of the subjects sampled. The two most prevalent rDNA sequence types that were identified in multiple individuals corresponded closely to those of Alloiococcus otitis and Corynebacterium otitidis, commonly thought to be associated exclusively with infections of the middle ear. Our results suggest, therefore, that the outer ear canal may serve as a reservoir for normally commensal microbes that can contribute to pathogenesis upon introduction into the middle ear. Alternatively, culture analyses of diseases of the middle ear may have been confounded by these contaminating commensal organisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".