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Record W1988205773 · doi:10.1002/hed.21042

Quality of life in patients with maxillectomy prostheses

2009· article· en· W1988205773 on OpenAlexaff
Jonathan C. Irish, Nimani Sandhu, Colleen Simpson, Robert E. Wood, Ralph Gilbert, Patrick Gullane, Dale Brown, David Goldstein, Gerald M. Devins, Emma Barker

Bibliographic record

VenueHead & Neck · 2009
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineQuality of life (healthcare)SwallowingRehabilitationIntrusivenessPhysical therapyDentistryPsychologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: This cross-sectional study sought to determine patient quality of life and function after prosthetic rehabilitation for maxillary and palate defects following cancer resection. METHODS: Sixty-nine patients were identified, 42 participated in the study (61%). The Obturator Functioning Scale (OFS) and 4 general quality of life measures (Mental Health Inventory [MHI], Impact of Events Scale [IES], Illness Intrusiveness Ratings Scale [IIRS], and Centre for Epidemiologic Studies Depression Scale [CES-D]) were correlated with clinical parameters. RESULTS: Leakage when swallowing foods was the most frequently reported problem with the obturator (29%). Positive correlation was noted between the OFS and both the IES subscales (p < .01) and CES-D (p < .001). Difficulty with speech and eating was associated with increased avoidance of social situations. The surgical approach had a significant effect on the OFS, IES, and MHI subscales (p < .01). CONCLUSION: These results support the findings that good obturator function is associated with a better quality of life.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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

Citations141
Published2009
Admission routes1
Has abstractyes

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