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Record W2895524284 · doi:10.21037/jtd.2018.09.71

Digital pleural drainage technology is here to stay—time to realize its potential

2018· letter· en· W2895524284 on OpenAlexaff
Ching Yeung, Sébastien Gilbert

Bibliographic record

VenueJournal of Thoracic Disease · 2018
Typeletter
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineLimitingDrainageHealth careReliability (semiconductor)DocumentationChest tubeMedical emergencyIntensive care medicineOperations managementSurgeryEngineeringPneumothoraxComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Institutions worldwide seek means to continue to produce high quality care while reducing overall health care cost. Digital pleural drainage devices have been explored as a potential method to improve health care efficiency. Objective documentation of a parenchymal air leak using digital sensors results in increased inter-observer reliability (1,2) thus presenting an opportunity to identify candidates for chest tube removal in a more reliable and timely fashion. With indwelling chest tubes often being a limiting factor in discharging patients from hospital, earlier removal may lead to improved length of stay.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.015
GPT teacher head0.305
Teacher spread0.290 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
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

Citations1
Published2018
Admission routes1
Has abstractyes

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