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Record W1991411164 · doi:10.3109/00016489.2011.617780

Time trend analysis of mastoidectomy procedures performed in Ontario, 1987–2007

2011· article· en· W1991411164 on OpenAlexaffabout
Prodip K. Das‐Purkayastha, Chris Coulson, David D. Pothier, Phillip Lai, John Rutka

Bibliographic record

VenueActa Oto-Laryngologica · 2011
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMastoidectomyMedicineSurgeryCholesteatoma

Abstract

fetched live from OpenAlex

CONCLUSIONS: There has been a reduction in the number of modified radical mastoidectomy and revision mastoidectomy surgeries per head of population in Ontario between 1987 and 2007, we believe that this represents a true reduction in prevalence of cholesteatoma. The increase of cases performed at the University Hospital Network, Toronto (UHN) may represent a shift to subspecialization in the treatment of chronic ear disease. OBJECTIVE: To analyze the trends in mastoid operations for chronic middle ear disease in the Canadian province of Ontario between 1987 and 2007 and to determine whether an increasing proportion of these procedures are being performed in tertiary referral centres. METHODS: The year on year population and number of mastoid procedures performed per year in Ontario and at the UHN between 1987 and 2007 were obtained from Statistics Canada and the Ministry of Health and Long-Term Care, Ontario, respectively. Population-adjusted rates of mastoid surgery for Ontario and the UHN. These data were collated and graphically represented for trend analysis. RESULTS: The population-adjusted number of mastoid procedures for Ontario declined from 7.1 cases per 100,000 in 1986 to 4.1 cases per 100,000 in 2006. During this time the number of both modified radical mastoidectomies and revision mastoid surgeries at UHN increased.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0230.000

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 head0.031
GPT teacher head0.237
Teacher spread0.206 · 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

Citations9
Published2011
Admission routes2
Has abstractyes

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