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Record W1591541241 · doi:10.1002/hed.23243

Scoping review of the literature on shoulder impairments and disability after neck dissection

2013· article· en· W1591541241 on OpenAlexafffund
David P. Goldstein, Jolie Ringash, Éric Bissada, Yves Jaquet, Jonathan C. Irish, Douglas B. Chepeha, Aileen M. Davis

Bibliographic record

VenueHead & Neck · 2013
Typearticle
Languageen
FieldMedicine
TopicNerve Injury and Rehabilitation
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer CentreUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineNeck dissectionAccessory nerveQuality of life (healthcare)MEDLINEDissection (medical)Physical therapyRehabilitationPhysical medicine and rehabilitationSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: The purpose of this article was to provide a review of the literature on shoulder disability after neck dissection. METHODS: A literature review was performed using Ovid Medline and Embase databases. A total of 306 abstracts and 78 full-text articles were reviewed. Forty-two articles were eligible for inclusion. RESULTS: Patients undergoing nerve-sacrifice neck dissections have greater disability and lower quality of life scores than those undergoing neck dissections with the least manipulation (ie, selective neck dissections). Shoulder impairments can still occur in patients undergoing selective neck dissections. Disability typically improves over time in patients undergoing nerve-sparing neck dissections. CONCLUSION: There was significant variability in the literature in terms of the prevalence and recovery of shoulder morbidity after neck dissection. This variability may not just be related to surgical technique or rehabilitation, but also to study design, definitions, and the variability in disability questionnaires used.

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)0.007
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.029
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.037
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0290.025
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.012
GPT teacher head0.327
Teacher spread0.315 · 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 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

Citations56
Published2013
Admission routes2
Has abstractyes

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