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Record W2137334501 · doi:10.1097/mcp.0b013e328121447d

Use of tunneled pleural catheters for outpatient treatment of malignant pleural effusions

2007· review· en· W2137334501 on OpenAlexaff
David R. Stather, Alain Tremblay

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2007
Typereview
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsSouth Health CampusUniversity of Calgary
Fundersnot available
KeywordsMedicineMalignant pleural effusionPleural effusionIntensive care medicinePleural fluidSurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Malignant pleural effusion is a common complication in advanced malignancy that causes debilitating symptoms which result in impaired quality of life. The primary therapeutic goal in malignant pleural effusion management is effective palliation of the associated respiratory symptoms. Pleurodesis by chest tube or thoracoscopy is widely accepted as the gold standard treatment, although these treatments are not without problems. Tunneled pleural catheters represent a new safe and effective outpatient treatment option for these patients, with no reported mortality and minimal morbidity. RECENT FINDINGS: Chest tube insertion with talc slurry and thoracoscopy with talc insufflation are effective methods for achieving spontaneous pleurodesis, although associated with significant morbidity and mortality. A growing body of evidence is confirming that long-term palliation of malignant pleural effusion can be achieved by using tunneled pleural catheters in a large proportion of relatively unselected patients on an outpatient basis. SUMMARY: The optimal method for palliative management of malignant pleural effusion remains controversial. The high success rates, low complication rates and efficacy in patients with a wide range of performance status support the use of tunneled pleural catheters as a first-line treatment for symptomatic malignant pleural effusion.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
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 head0.367
GPT teacher head0.450
Teacher spread0.082 · 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 designOther design
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

Citations17
Published2007
Admission routes1
Has abstractyes

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