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Record W7135935456

Forekomst af parodontitis og FORL hos perserkatte

2007· article· da· W7135935456 on OpenAlexaff
Maria L. Topholm Flørnæs, Mette Lund, Hanne Ellen Kortegaard, J. Arnbjerg

Bibliographic record

VenueResearch at the University of Copenhagen (University of Copenhagen) · 2007
Typearticle
Languageda
FieldBiochemistry, Genetics and Molecular Biology
Topicdental development and anomalies
Canadian institutionsPrivy Council Office
Fundersnot available
KeywordsGingivitisMEDLINESigns and symptoms
DOInot available

Abstract

fetched live from OpenAlex

I dette studie er omfanget af tandrelaterede sygdomme heriblandt parodontitis og feline odontoklastiske resorptions læsioner (FORL) blandt 19 perserkatte (heraf 2 exotics) på 3 til 9 år undersøgt både klinisk og radiografisk. Ingen af kattene havde tidligere fået renset tænder i anæstesi. Alle katte havde gingivitis (blødning ved sondering) på mindst en tand. Alle katte havde klinisk parodontitis på mindst 4 tænder, og det sås oftest i kindtænder. 26& havde svær klinisk parodontitis med klinisk fæstetab > 3 mm. Der sås tendens til højere forekomst af svær klinisk parodontitis med stigende alder, og den yngste kat i undersøgelsen med svær klinisk parodontitis var 4 år. Prævalensen for parodontitis vurderet radiologisk (alveolært knogletab) fandtes til 58%, og den yngste kat i undersøgelsen med alveolært knogletab var 3 år. Prævalensen for FORL var 47%, og der sås signifikant flere resorptioner i incisiver end i de resterende tandtyper. Samtidig signifikant flere FORL i maxillære fortænder og hjørnetænder end tilfældet var for mandiblens. Den yngste kat med FORL var 3 år, og der sås en tendens til stigende prævalens af FORL med stigende alder. Størstedelen af resorptionerne fandtes ved røntgen. 47% af de undersøgte dyr havde kronefrakturer, og knap halvdelen af disse (44%) var komplicerede. 37% af kattene blev henvist til ekstraktion af en eller flere tænder.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1720.034

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 head0.024
GPT teacher head0.261
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

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