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Record W2742802120 · doi:10.1183/16000617.0072-2017

Supplemental oxygen and dypsnoea in interstitial lung disease: absence of evidence is not evidence of absence

2017· letter· en· W2742802120 on OpenAlexfundno aff
Emily C. Bell, Narelle S. Cox, Nicole Goh, Ian Glaspole, Glen Westall, Alice Watson, Anne E. Holland

Bibliographic record

VenueEuropean Respiratory Review · 2017
Typeletter
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaMichael Smith Health Research BC
KeywordsMedicineBlindingInterstitial lung diseaseLung diseaseIntensive care medicineSelection biasEvidence-based medicinePublication biasSupplemental oxygenOxygen therapyQuality of evidencePhysical therapyEvidence-based practiceClinical trialRandomized controlled trialMeta-analysisLungInternal medicineAlternative medicinePathology

Abstract

fetched live from OpenAlex

We would like to thank M.R. Schaeffer and colleagues for their correspondence regarding our recently published systematic review of oxygen for interstitial lung disease (ILD) [1]. In this review, we found no consistent evidence that oxygen therapy administered during exercise tests reduced the primary outcome of dyspnoea, although randomised crossover trials demonstrated improvements in exercise performance. We also reported that the quality of evidence was very poor, due to the retrospective nature of many studies, the potential for selection bias and lack of blinding. Current evidence does not provide definitive answers regarding the benefits (or otherwise) of oxygen therapy for ILD

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)0.037
metaresearch head score (Gemma)0.230
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.037
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.230
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0030.005
Open science0.0030.002
Research integrity0.0120.008
Insufficient payload (model declined to judge)0.0080.003

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 head0.142
GPT teacher head0.392
Teacher spread0.250 · 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
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

Citations3
Published2017
Admission routes1
Has abstractyes

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