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Record W7072555751

Wound dressings during cancer radiotherapy : a survey of Canadian practice

2013· article· en· W7072555751 on OpenAlexaboutno aff

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGlass properties and applications
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisContext (archaeology)Descriptive statisticsRadiation therapyNursing practiceClinical PracticePractice nurse
DOInot available

Abstract

fetched live from OpenAlex

Patients who undergo radiotherapy typically experience changes to the skin in the area where the treatment is administered. Some discussions exist whether radiating through wound dressings will cause a boost effect to patients with cancer. The purpose of the thesis is to describe the current evidence and practice through literature review and a national environmental scan with the aim of developing the foundation for further research. After Research Ethics Boards approvals, one nurse per 34 Radiation Oncology Centres in Canada was contacted. Current practice survey data were collected from 18 Centres. Telephone interviews were conducted with four nurse participants to understand the context of nursing practice environment. Data analysis was done using descriptive statistics for the survey data and thematic analysis for the interviews. The integrative results of the study were reviewed with five inter-professional experts. The study results are used to make recommendations for research, practice, leadership and policy.

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)0.010
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 score0.042
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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