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Record W2488510469 · doi:10.1182/blood.v110.11.412.412

Prolonged Overall Survival with Lenalidomide Plus Dexamethasone Compared with Dexamethasone Alone in Patients with Relapsed or Refractory Multiple Myeloma.

2007· article· en· W2488510469 on OpenAlexaff
Donna Weber, Robert Knight, Christine Chen, Andrew Spencer, Zhinuan Yu, Jerome B. Zeldis, Marta Olesnyckyj, Meletios Α. Dimopoulos

Bibliographic record

VenueBlood · 2007
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDexamethasoneMedicineLenalidomideThalidomidePlaceboRefractory (planetary science)Multiple myelomaInternal medicineCorticosteroidPhases of clinical researchGastroenterologyUrologySurgeryChemotherapy

Abstract

fetched live from OpenAlex

Abstract Introduction: Lenalidomide (Len), an analog of thalidomide (Thal) is a novel, oral, immunomodulatory agent that is effective against multiple myeloma (MM). In 2 prospective, randomized, double-blind, placebo-controlled phase III trials, Len with dexamethasone (Dex) induced a significantly higher overall response (OR) rate and complete remission (CR) rate, as well as longer time-to-progression (TTP) in comparison with Dex alone. Here, we investigate the long-term overall survival (OS) with Len/Dex. Methods: We evaluated the pooled results from both randomized trials (MM-009, MM-010) of 704 patients who had relapsed or refractory MM, without prior resistance to Dex, who received either Len (25 mg daily for 3 weeks every 4 weeks), or placebo. Dex was given at 40 mg on days 1–4, 9–12, 17–20 every 4 weeks for 4 cycles. From cycle 5 onwards, Dex was given at 40 mg on days 1–4 only. Response rate and TTP are based on data obtained before unblinding (June 2005 [MM-009] and August 2005 [MM-010]). Follow-up data on OS were obtained up to January 2007. Forty-seven percent of patients who received placebo/Dex crossed over to receive Len +/− Dex. Results: Of 704 patients, 353 were treated with Len/Dex and 351 with Dex alone. Baseline characteristics were well balanced between patients receiving Len/Dex and those receiving Dex alone. Median TTP, OR, and CR were significantly improved in patients treated with Len/Dex compared with Dex alone (Table). Of patients who progressed on Dex alone prior to unblinding, or were found to be receiving Dex alone after unblinding, 47% crossed over to Len +/− Dex. Despite these patients crossing over to Len +/− Dex at progression or at the time of unblinding, the OS was significantly improved in patients treated with Len/Dex compared with Dex alone (hazard ratio 1.295; 95% confidence interval 1.040–1.614; p=0.02). Median OS in the Len/Dex group was 35 months and 31 in the Dex alone group (p<0.05). Median OS was also significantly longer with Len/Dex compared with Dex alone in patients with more than 1 prior therapy (32.4 months versus 27.3 months, p<0.05). Similar median OS was observed with Len/Dex and Dex alone in patients with 1 prior therapy (median OS not yet reached and 35.3 months, p=0.24). Conclusion: With increased follow-up and despite cross-over, patients treated first with Len/Dex had significantly improved OS compared with those treated with Dex alone. Len/Dex (n=353) Dex alone (n=351) P value OR, % 60.6 21.9 <0.001 CR, % 15.0 2.0 <0.001 Median TTP, months 11.2 4.7 <0.001 Median OS, months 35.0 31.0 <0.05 Median OS in patients with 1 prior treatment, months not yet reached 35.3 0.24 Median OS in patients with >1 prior treatment, months 32.4 27.3 <0.05

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.027
GPT teacher head0.274
Teacher spread0.248 · 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 designRandomized trial
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

Citations35
Published2007
Admission routes1
Has abstractyes

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