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Record W2617309336 · doi:10.1007/s00455-017-9809-z

Reflections on Clinical and Statistical Use of the Penetration-Aspiration Scale

2017· review· en· W2617309336 on OpenAlexaff
Catriona M. Steele, Karen Grace‐Martin

Bibliographic record

VenueDysphagia · 2017
Typereview
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity Health NetworkToronto Rehabilitation Institute
FundersNational Institute on Deafness and Other Communication Disorders
KeywordsCategorical variableOrdinal ScaleMedicineDysphagiaLogistic regressionScale (ratio)Ordinal dataSwallowingOtorhinolaryngologyStatisticsEconometricsSurgeryMathematicsInternal medicineCartography

Abstract

fetched live from OpenAlex

The 8-point Penetration-Aspiration Scale (PAS) was introduced to the field of dysphagia in 1996 and has become the standard method used by both clinicians and researchers to describe and measure the severity of airway invasion during swallowing. In this article, we review the properties of the scale and explore what has been learned over 20 years of use regarding the construct validity, ordinality, intervality, score distribution, and sensitivity of the PAS to change. We propose that a categorical revision of the PAS into four levels of increasing physiological severity would be appropriate. The article concludes with a discussion of common errors made in the statistical analysis of the PAS, proposing that frequency distributions and ordinal logistic regression approaches are most appropriate given the properties of the scale. A hypothetical dataset is included to illustrate both the problems and strengths of different statistical approaches.

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)0.250
metaresearch head score (Gemma)0.433
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.250
Threshold uncertainty score0.925

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2500.433
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.008
Science and technology studies0.0010.015
Scholarly communication0.0080.010
Open science0.0050.004
Research integrity0.0040.014
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.570
GPT teacher head0.637
Teacher spread0.067 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations197
Published2017
Admission routes1
Has abstractyes

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