Evolution and Otitis Media: A Review, and a Model to Explain High Prevalence in Indigenous Populations
Bibliographic record
Abstract
Otitis media (OM; inflammation of the middle ear) comprises a group of disorders that are among the most common disorders of childhood. OM is also heritable and has effects on fecundity. This means that OM is subject to evolution, yet the evolutionary forces that may determine susceptibility to OM have not been adequately explored. Here I analyze evolutionary forces that may determine susceptibility to middle ear inflammation. These forces include those affecting function of the middle ear, host immunity, or colonization by and pathogenicity of bacteria. I review existing evolutionary models of host-pathogen interaction and coevolution and apply these to better understand the complex evolutionary landscape of middle ear infection and inflammation in humans, including factors determining transition between stable evolutionary strategies for host and bacteria. This understanding is then applied to an analysis of OM in indigenous populations to devise a new theory for OM prevalence in Australian Aborigine, Native American, Inuit, and Maori populations. I suggest that high prevalence in such groups may have resulted from encounters of these previously isolated populations with European immigrants in the 15th and 16th centuries. This exposed them to new strains of bacteria to which their immune system had not evolved immunity, perturbing a previously stable host-pathogen coevolutionary state.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".