MétaCan
Menu
Back to cohort

Maxillary sinusitis as an indicator of respiratory health in past populations

2000· article· en· W2002825156 on OpenAlexaffabout
Deborah C. Merrett, Susan Pfeiffer

Bibliographic record

VenueAmerican Journal of Physical Anthropology · 2000
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversity of TorontoUniversity of Manitoba
Fundersnot available
KeywordsSinusitisMedicineRespiratory systemPopulationMaxillaConfoundingDentistryPathologyInternal medicineSurgeryEnvironmental health

Abstract

fetched live from OpenAlex

Chronic infectious respiratory disease in a past human population is investigated through the quantification of maxillary sinusitis among Iroquoian horticulturists. Three hundred forty-eight right and left maxillae of a Southern Ontario Iroquoian skeletal sample, Uxbridge Ossuary, ca. AD 1440, were examined for evidence of chronic infection (minimum number of individuals = 207: 114 adults, 22 adolescents, 38 juveniles and 33 infants). Modern clinical criteria were applied to differentiate lesions of respiratory and dental origin. Osseous lesions of the maxillary sinuses were observed in 50% of the individuals examined. These lesions are morphologically consistent with nonspecific lesions observed in other past populations that have been attributed to the presence of pathogens. The prevalence of maxillary sinusitis increases with age. Osseous changes suggestive of maxillary sinusitis of respiratory origin are at a maximum prevalence in juveniles and adolescents. In adults, infection of dental origin becomes a confounding factor in the identification of sinusitis of respiratory origin. Fifteenth century Iroquoians were experiencing high airborne pathogen levels and poor indoor air quality. The prevalence of maxillary sinusitis and the exploration of the origin of tissue injury may contribute to our reconstruction of the quality of life and the respiratory health status of past human populations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.371
Teacher spread0.349 · 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.

Study designObservational
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

Citations70
Published2000
Admission routes2
Has abstractyes

Explore more

Same venueAmerican Journal of Physical AnthropologySame topicSinusitis and nasal conditionsFrench-language works237,207