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What Are the Attitudes and Beliefs of Oncologists Regarding Potential Cancer Rehabilitation in a Tertiary Cancer Center?

2019· article· en· W2939594655 on OpenAlexaff
George J. Francis, Jack B. Fu

Bibliographic record

VenueRehabilitation Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsFoothills Medical CentreUniversity of Calgary
FundersNational Cancer Institute
KeywordsRehabilitationDeconditioningMedicineQuality of life (healthcare)CancerOperationalizationPhysical therapyFamily medicineGerontologyNursing

Abstract

fetched live from OpenAlex

Cancer Rehabilitation (CR) is an emerging field in Physical Medicine & Rehabilitation. Current literature highlights the effectiveness of cancer rehabilitation in improving functional outcomes, shorter length of hospital stay, and improved quality of life. Despite this, there are very few formalized CR programs across all of North America. We conducted a survey at a tertiary cancer center without a formalized CR program to assess the perceived need of such a program and its potential development. This survey of medical, surgical, radiation and pediatric oncologists demonstrated that 92.3% of 39 respondents felt CR was somewhat to very important, particularly for their patients' issues of fatigue, deconditioning, pain management and disposition planning. These findings highlight the value seen by oncologists in the need for further cancer rehabilitation access and formalized program development in order to meet patient needs for improving functional deficits, activities of daily living and 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)0.003
metaresearch head score (Gemma)0.013
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.016
GPT teacher head0.362
Teacher spread0.346 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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