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Record W2121793416 · doi:10.2217/cer.12.69

Incidence of skeletal morbidity rates over time in patients with multiple myeloma-related bone disease as reported in randomized trials employing bone-modifying agents

2012· review· en· W2121793416 on OpenAlexaff
Michael Poon, Liang Zeng, Liying Zhang, Janey Hsiao, Erin Wong, Henry Lam, Gillian Bedard, Edward Chow

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2012
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineMultiple myelomaIncidence (geometry)Internal medicineBone diseaseRandomized controlled trialSurgeryOncologyOsteoporosis

Abstract

fetched live from OpenAlex

AIM: The purpose of this review was to investigate if advances in bone-targeted therapies have decreased the incidence of skeletal morbidity rates over time in patients with multiple myeloma-related bone disease. METHODS: A literature search was conducted over the OvidSP platform in MEDLINE, EMBASE and Cochrane Central Register of Controlled Trials to identify Phase III results from bone-targeted therapy trials in patients with multiple myeloma. The skeletal morbidity rate was the end point of interest, and for each study, a mean year of enrollment ([start of enrollment + end of enrollment]/2) was calculated. RESULTS: A total of eight study arms were identified, with only two placebo arms; therefore, a weighted linear regression was not feasible and only intervention treatment arms were analyzed. A statistically significant downward trend in the skeletal morbidity rate was observed in all intervention arms. CONCLUSION: The incidence of skeletal morbidity rates has decreased significantly over time in patients with multiple myeloma.

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)0.033
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0130.002
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.248
GPT teacher head0.514
Teacher spread0.266 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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