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Record W22226276 · doi:10.1177/193229681100500630

Elastic strain-sharing as a means of fabricating strained-Si(110) nanomembranes

2007· article· en· W22226276 on OpenAlexaboutno aff
Shelley A. Scott, Arrielle Opotowsky, D. E. Savage, Michelle Roberts, M. G. Lagally

Bibliographic record

VenueBulletin of the American Physical Society · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceStrain (injury)Composite materialEngineering physicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

The Coalition for Clinical Research--Self-Monitoring of Blood Glucose Scientific Board convened a meeting in San Francisco, CA, July 20-21, 2011, to discuss the current practice of self-monitoring of blood glucose (SMBG) in non-insulin-treated (NIT) type 2 diabetes mellitus (T2DM). Twelve physician panel members from academia, practice, and government attended this meeting. These experts came from the United States, Brazil, Canada, France, Germany, Italy, and the United Kingdom. In addition, three consultants from Australia, Germany, and the United States contributed to the group's final report. This coalition was organized by Diabetes Technology Society. Self-monitoring of blood glucose was studied from eight perspectives related to patients with NIT T2DM: (1) epidemiological studies; (2) randomized controlled trials (RCT)s and meta-analyses; (3) targets, timing, and frequency of SMBG use; (4) incidence and role of SMBG in preventing hypoglycemia with single-drug regimens and combination regimens consisting of antihyperglycemic agents other than secretagogues and insulin; (5) comparison of SMBG with continuous glucose monitoring; (6) technological capabilities and limitations of SMBG; (7) barriers to appropriate use of SMBG; and (8) methods and end points for appropriate future clinical trials. The panel emphasized recent studies, which reflect the current approach for applying this intervention. Among the participants there was consensus that: SMBG is an established practice for patients with NIT T2DM, and to be most effective, it should be performed in a structured format where information obtained from this measurement is used to guide treatment; New, high-quality efficacy data from RCTs have demonstrated efficacy of SMBG in NIT T2DM in trials reported since 2008; Both patients and health care professionals require education on how to respond to the data for SMBG to be effective; and Additional well-defined studies are needed to assess the benefits and costs of SMBG with end points not limited to hemoglobin A1c.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.006
GPT teacher head0.224
Teacher spread0.218 · 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 designBench or experimental
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

Citations0
Published2007
Admission routes1
Has abstractyes

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