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Record W2168754575

Economic and Clinical Net Benefits from the Use of Samarium Sm-153 Lexidronam Injection in the Treatment of Bone Metastases

2013· article· en· W2168754575 on OpenAlexaffabout
D. Wayne Taylor

Bibliographic record

VenueInternational Journal of Tumor Therapy · 2013
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineBone painPain reliefPhysical therapySurgery
DOInot available

Abstract

fetched live from OpenAlex

Samariu m Sm-153 Lexid ronam Inject ion (Samariu m-153) is used to treat bone pain that results when cancer spreads to the bone. It has been on the market since 1997 but still is not funded by any one of the 10 p rovincial cancer care authorities in Canada. The only cost-effectiveness study using randomized control trial data of Samariu m-153 for pain relief in patients with bone metastases clearly demonstrated that Samariu m-153 was a dominant therapy. At the time of writ ing, the product cost of Samariu m-153 for one patient treatment was $4,500. At that price, in 2012, the total product cost for all of the projected 755 patient treatments in Canada would be $3,397,500. For an investment of $3,397,500 in Samariu m-153, system savings of $7,758,905 could be realized for a return on investment (ROI) of 228%. Overall treat ment cost-savings would have accrued while Samariu m-153 p rovided suffering cancer patients with equal or better care and clinical outcomes, as measured by pain relief and quality of life.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.091
GPT teacher head0.365
Teacher spread0.274 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

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