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Record W4241563488 · doi:10.1111/resp.13699_249

MIPAF STUDY: MALAYSIAN OBSERVATIONAL STUDY TO DESCRIBE IDIOPATHIC PULMONARY FIBROSIS (IPF) BASELINE PRESENTATION AND OUTCOME WITH ANTI FIBROTIC

2019· article· en· W4241563488 on OpenAlexaff
Syazatul Syakirin, Sirol Aflah, NOOR SHAHIRA MD YUSOFF, Mohammad Jamil, Khoo Yi, Noorul Afidza Muhammad, Yoshikazu Inoue, Athol U. Wells, Jin Woo Song, Zuojun Xu, Hideya Kitamura, Takafumi Suda, Masaki Okamoto, Rozsa Schlenker‐Herceg, Martin Kolb, Kevin K. Brown, Manuel Quaresma

Bibliographic record

VenueRespirology · 2019
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineIdiopathic pulmonary fibrosisObservational studyPresentation (obstetrics)Baseline (sea)Internal medicinePulmonary fibrosisFibrosisLungSurgery

Abstract

fetched live from OpenAlex

Conclusions: Pirfenidone showed an acceptable safety and provide consistent treatment effect irrespective of IPF disease severity in real-world setting.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.322
Teacher spread0.274 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2019
Admission routes1
Has abstractyes

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