MétaCan
Menu
Back to cohort
Record W3157433726 · doi:10.1177/01945998211010435

Determining the Impact of Thickened Liquids on Swallowing in Patients Undergoing Irradiation for Oropharynx Cancer

2021· article· en· W3157433726 on OpenAlexaff
Carly E. A. Barbon, Douglas B. Chepeha, Andrew Hope, Melanie Péladeau-Pigeon, Ashley A. Waito, Catriona M. Steele

Bibliographic record

VenueOtolaryngology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of Health
KeywordsDysphagiaSwallowingMedicinePenetration (warfare)Head and neck cancerRadiation therapyResidue (chemistry)PopulationSurgeryInternal medicineChemistry

Abstract

fetched live from OpenAlex

The current standard for the treatment of oropharynx cancers is radiation therapy. However, patients are frequently left with dysphagia characterized by penetration‐aspiration (impaired safety) and residue (impaired efficiency). Although thickened liquids are commonly used to manage dysphagia, we lack evidence to guide the modification of liquids for clinical benefit in the head and neck cancer population. The objective of this study was to assess the impact of slightly and mildly thick liquids on penetration‐aspiration and residue in 12 patients with oropharyngeal cancer who displayed penetration‐aspiration on thin liquid within 3 to 6 months after completion of radiotherapy. Significantly fewer instances of penetration‐aspiration were seen with slightly and mildly thick liquids as compared with thin ( P <. 05). No differences were found across stimuli in the frequency of residue. Patients with oropharyngeal cancers who present with post–radiation therapy dysphagia involving penetration‐aspiration on thin liquids may benefit from slightly and mildly thick liquids without risk of worse residue.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.424
Teacher spread0.384 · 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.

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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOtolaryngologySame topicDysphagia Assessment and ManagementFrench-language works237,207