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Record W4394617236 · doi:10.1016/j.xjtc.2024.04.001

Drainology: Leveraging research in chest-drain management to enhance recovery after cardiothoracic surgery

2024· editorial· en· W4394617236 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJTCVS Techniques · 2024
Typeeditorial
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersMedela
KeywordsMedicineCardiothoracic surgeryRandomized controlled trialCardiac surgeryIntensive care unitCoronary artery bypass surgeryBypass graftingIntensive care medicineQuality managementMedical emergencyArterySurgeryOperations management

Abstract

fetched live from OpenAlex

CABG coronary artery bypass grafting CPPF continuous posterior pericardial flushing ICU intensive care unit LOS length of stay POAF postoperative atrial fibrillation RCT randomized controlled trial SOC standard of care Central Message: Wide variation in the use of chest drains after cardiothoracic surgery can compromise consistency of care and outcomes.The new science of drainology represents an opportunity for quality improvement.Perspective Statement: Current practices for chest drain management are typically based on clinical tradition due to a lack of quality evidence.An iterative process of reviewing new research, building standardized best practices around the findings, identifying knowledge gaps, and conducting new studies designed to bridge them is needed to improve postoperative care and patient outcomes after cardiothoracic surgery.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.032
GPT teacher head0.400
Teacher spread0.368 · 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