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Record W2618109307 · doi:10.21037/apm.2017.04.08

Should dexamethasone be standard in the prophylaxis of pain flare after palliative radiotherapy for bone metastases?—a debate

2017· article· en· W2618109307 on OpenAlexaff
Mark Niglas, Srinivas Raman, Danielle Rodin, Jay Detsky, Carlo DeAngelis, Hany Soliman, Edward Chow, May Tsao

Bibliographic record

VenueAnnals of Palliative Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsSunnybrook Health Science CentreSunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineIncidence (geometry)Randomized controlled trialPlaceboAnxietyDexamethasoneQuality of life (healthcare)Palliative careClinical trialRadiation therapyInternal medicinePhysical therapyAlternative medicinePsychiatry

Abstract

fetched live from OpenAlex

Pain flare is a well-recognized side-effect of palliative radiotherapy for the treatment of painful bone metastases, with recent randomized data showing incidence rates up to 35%. The impact of pain flare has been associated with worsening immobility, anxiety, depression and quality of life. The use of dexamethasone has recently been supported as an effective option in reducing radiation-induced pain flare based on the NCIC Clinical Trials Group (NCIC CTG) Symptom Control 23 (SC.23) randomized double-blind placebo-controlled trial. Despite this, conflicting opinions exist, and standard clinical use of dexamethasone to prevent pain flare continues to be debated among clinicians. Given this controversy, two sides of the debate are presented. Although consensus has not been achieved, the choice to use dexamethasone in the prophylactic setting to reduce pain flare incidence should be a shared decision between the oncologist and patient. Factors including symptom burden, comorbidities, performance status, quality of life and radiation dose and fractionation should be taken into account on an individualized level.

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)0.007
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.393
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.171
GPT teacher head0.431
Teacher spread0.260 · 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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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