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Record W4230963055 · doi:10.1001/jama.298.9.973-a

Incorrect Data and Wording in: Patient-Reported Symptoms and Quality of Life During Treatment With Tamoxifen or Raloxifene for Breast Cancer Prevention: The NSABP Study of Tamoxifen and Raloxifene (STAR) P-2 Trial

2007· article· en· W4230963055 on OpenAlexaff
Robert A. Harrington, John H. Alexander, Judith S. Hochman, Harmony R. Reynolds, Vladimír Džavík, Frans Van de Werf

Bibliographic record

VenueJAMA · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEstrogen and related hormone effects
Canadian institutionsUniversity of Toronto
FundersProcter and Gamble
KeywordsRaloxifeneMedicineTamoxifenBreast cancerOncologySelective estrogen receptor modulatorAntiestrogenQuality of life (healthcare)Internal medicineGynecologyCancer

Abstract

fetched live from OpenAlex

ent effects at the cellular level.L-NAME is relatively nNOS and eNOS selective.However, it is not clear that L-NAME is more potent in vivo, especially since it is believed that iNOS is the important target in patients with cardiogenic shock.4 Refractory shock is characterized by lower-thanexpected systemic vascular resistance and hypotension, both potential effects of excess NO. 5 Effects of NOS inhibition in patients with cardiogenic shock should differ from normal volunteers.Importantly, the initial positive singlecenter experience with NOS inhibition in patients with cardiogenic shock used L-NMMA. 1 We believe our conclusion is valid: L-NMMA, at the dose and duration studied in TRIUMPH, had no effect on mortality.It is possible that other dosing strategies, perhaps taking into consideration baseline renal function, or other NOS inhibitors might have a different effect.Determining this would require another large randomized clinical trial with mortality outcomes.

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)0.011
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.055
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0210.004

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 head0.028
GPT teacher head0.317
Teacher spread0.289 · 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.

Study designNot applicable
DomainReporting
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

Citations0
Published2007
Admission routes1
Has abstractno

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