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Record W2001015389 · doi:10.1051/medsci/20122811021

Le réseau de télépathologie de l’Est du Québec

2012· review· fr· W2001015389 on OpenAlexaffabout
Bernard Têtu, J.-C. Boulanger, Christine Houde, Jean‐Paul Fortin, Marie‐Pierre Gagnon, Geneviève Roch, Guy Paré, Marie-Claude Trudel, Claude Sicotte

Bibliographic record

Venuemédecine/sciences · 2012
Typereview
Languagefr
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsHEC MontréalUniversité de MontréalUniversité LavalWilfrid Laurier UniversityCentre hospitalier de l'Université LavalCentre hospitalier universitaire de QuébecHôpital du Saint-Sacrement
Fundersnot available
KeywordsTelepathologyVideoconferencingTelemedicineSecond opinionComputer scienceMedicineTelecommunicationsHealth carePathologyPolitical science

Abstract

fetched live from OpenAlex

The aim of the Eastern Québec telepathology network is to provide uniform diagnostic telepathology services across a huge geographic region with a low population density. This project is intended to provide surgeons and pathologists with frozen section and second opinion services anywhere and at any time across the entire region, in order to avoid unnecessary patient transfer. The project has been implemented in 21 sites, each equipped with a whole slide scanner, a macroscopy station, a videoconferencing device and a viewer/case management and collaboration solution. Of the 21 sites, 6 are devoid of a pathology laboratory, two have no pathologist and 5 have only one pathologist on site. Signs of improvement of medical care in this region are already apparent since the Eastern Québec telepathology network has been implemented. However, it is important not to underestimate the challenges related to change management in the course of implementation of such a new technology.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.709
Threshold uncertainty score0.586

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.426
GPT teacher head0.524
Teacher spread0.097 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations10
Published2012
Admission routes2
Has abstractyes

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