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Peer-to-Peer content search supported by a distributed index in a publication/search model

2006· article· en· W26889595 on OpenAlexaboutno aff
Gabriel Hernán Tolosa, Jorge Alberto Peri, Fernando Raúl Alfredo Bordignon

Bibliographic record

VenueJournal of Digital Information Management · 2006
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceSearch engine indexingPeer-to-peerNode (physics)Information retrievalIndex (typography)Set (abstract data type)Search engineHierarchyServerDistributed computingData miningTheoretical computer scienceComputer networkWorld Wide Web

Abstract

fetched live from OpenAlex

A challenge of modern cardiovascular medicine is to find new, effective treatments for patients with refractory angina pectoris (RAP), a clinical condition characterized by severe angina despite optimal medical therapy and "no option" for a surgical or percutaneous revascularization. Although the relevant advance of both pharmaceutical and interventional treatments for patients affected by symptomatic coronary artery disease has greatly contributed to prolong survival, the increasing number of patients experimenting persistent and invalidating angina symptoms, highlights that quality of life of these patients has not been equally improved. Clinical limitations of the efficiency of conventional and relatively new approaches justify the search for new therapeutic options. In this review, we will focus on the epidemiology of RAP, and we will provide a brief update on the different options actually available to these patients with particular interest to an innovative device that narrow the coronary sinus: the Reducer system (Neovasc Inc., Richmond B.C., Canada). The efforts of present and future clinical studies will ultimately answer the question of whether this intriguing therapy is a suitable strategy for treatment of patients with RAP.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.947
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.006
Open science0.0020.001
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 head0.030
GPT teacher head0.264
Teacher spread0.234 · 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.

Study designSimulation or modeling
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
Published2006
Admission routes1
Has abstractyes

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