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Record W2090181690 · doi:10.1111/ger.12098

The Seattle Care Pathway for securing oral health in older patients

2014· article· en· W2090181690 on OpenAlexaff
Iain Pretty, R.P. Ellwood, Edward Chin Man Lo, Michael I. MacEntee, Frauke Müller, Eric Rooney, W. Murray Thomson, Elisa M. Ghezzi, A.W.G. Walls, Mark S. Wolff

Bibliographic record

VenueGerodontology · 2014
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of British Columbia
FundersUniversity of ManchesterNational Institute for Health and Care ResearchColgate-Palmolive Company
KeywordsMedicineCare pathwayDependency (UML)Clinical pathwayGerontologyHealth careDental careCritical pathwaysWork (physics)NursingFamily medicineProcess management

Abstract

fetched live from OpenAlex

There is a need for a structured, evidence based approach to care for older dental patients. The following article describes the development of the Seattle Care Pathway based upon a workshop held in 2013. An overview is provided on the key issues of older persons dental care including the demography shift, the concept of frailty, the need for effective prevention and treatment to be linked to levels of dependency and the need for a varied and well educated work force. The pathway is presented in tabular form and further illustrated by the examples in the form of clinical scenarios. The pathway is an evidence based, pragmatic approach to care designed to be globally applicable but flexible enough to be adapted for local needs and circumstances. Research will be required to evaluate the pathways application to this important group of patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.003

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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations107
Published2014
Admission routes1
Has abstractyes

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