MétaCan
Menu
Back to cohort
Record W59796600

Methods of functional outcomes assessment following treatment of oral and oropharyngeal cancer: review of the literature.

2008· article· en· W59796600 on OpenAlexaff
Jana Rieger, Jeffrey Harris, Daniel A. O’Connell, Khalid Al‐Qahtani, Khalid Ansari, Jason Chau, Hadi Seikaly

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHumanitiesGynecologyMedicinePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this article is to review and document the differing methods of functional outcome measurements following treatment for oral and oropharyngeal carcinoma. STUDY DESIGN: Literature review. METHODS: Articles were identified using the MEDLINE database search engine. The "Methods" sections of relevant articles were then reviewed, and functional outcomes assessment methods were tabulated. RESULTS: We identified 60 articles published in the last 7 years (2000-2007) that reported on functional outcomes following treatment for oral or oropharyngeal cancer. Twenty-three studies used quality of life questionnaires and 12 used clinical observations to describe function. Swallowing was assessed objectively in 29 studies, with videofluoroscopic swallowing studies as the primary method of assessment. Speech was assessed in only 10 articles, with perceptual analysis used as the primary assessment modality. CONCLUSIONS: Preserving good speech and swallowing function following treatment for oral and oropharyngeal cancer remains an extremely important aspect of cancer care. Nevertheless, there is a clear lack of uniform methods for assessing functional outcomes. We propose that functional outcome studies should include both objective and subjective assessments of swallowing and speech to gain sufficient information on posttreatment function.

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)0.001
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 score0.168
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.122
GPT teacher head0.464
Teacher spread0.342 · 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

Citations47
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicDysphagia Assessment and ManagementFrench-language works237,207