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Record W2904562747 · doi:10.1200/jop.18.00379

Outcomes of Peer Review for Radiotherapy Treatment Plans With Palliative Intent

2018· article· en· W2904562747 on OpenAlexaff
Dina Thompson, Kimberly Cox, J. D. Loudon, Ivan Yeung, Woodrow Wells

Bibliographic record

VenueJournal of Oncology Practice · 2018
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsSouthlake Regional Health Center
Fundersnot available
KeywordsMedicinePalliative careMEDLINEPeer reviewRadiation therapyQuality of life (healthcare)Family medicineNursingSurgery

Abstract

fetched live from OpenAlex

PURPOSE: Peer review of a proposed treatment plan is increasingly recognized as an important quality activity in radiation medicine. Although peer review has been emphasized in the curative setting, applying peer review for treatment plans that have palliative intent is receiving increased attention. This study reports peer review outcomes for a regional cancer center that applied routine interprofessional peer review as a standard practice for palliative radiotherapy. METHODS AND MATERIALS: Peer review outcomes for palliative radiotherapy plans were recorded prospectively for patients who began radiotherapy between October 1, 2015, and September 30, 2017. Recommended and implemented changes were recorded. The content of detailed discussions was recorded to gain insight into the complexities of palliative treatment plans considered during peer review. RESULTS: Peer review outcomes were reviewed for 1,413 treatment plans with palliative intent. The proportions of detailed discussions and changes recommended were found to be 139 (9.8%) and 29 (2.1%), respectively. The content of detailed discussions and changes recommended was categorized. Major changes represented 75.9% of recommended changes, of which 84.2% were implemented clinically. CONCLUSION: Many complexities exist that are specific to palliative radiotherapy. Interprofessional peer review provides a forum for these complexities to be openly discussed and is an important activity to optimize the quality of care for patients with treatment plans that have palliative intent.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.761
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 head0.107
GPT teacher head0.474
Teacher spread0.367 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2018
Admission routes1
Has abstractyes

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