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Record W3207602276 · doi:10.52547/ibj.25.6.381

Serum Krebs von den Lungen-6 for Predicting the Severity of COVID-19 Lung Injury: A Systematic Review and Meta-Analysis

2021· review· en· W3207602276 on OpenAlexaboutno aff
Andro Pramana Witarto, Bendix Samarta Witarto, Achmad Januar Er Putra, Shidi Laras Pramudito, Alfian Nur Rosyid

Bibliographic record

VenueIranian Biomedical Journal · 2021
Typereview
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisCoronavirus disease 2019 (COVID-19)Internal medicineGastroenterologyLungSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakCochrane LibraryPathologyDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background: Lung injury is common in coronavirus disease 2019 (COVID-19) patients. The severity of lung injury appears to be reflected in serum Krebs von den Lungen-6 (KL-6), a glycoprotein expressed on type II alveolar epithelium. This study aims to assess the role of serum KL-6 in reflecting the severity of lung injury in COVID-19 patients. Methods: A systematic search was conducted in Scopus, PubMed, Wiley Online Library, and ProQuest. Articles were screened based on several eligibility criteria and assessed for study quality using Newcastle-Ottawa Scale. Results: This systematic review included four studies involving a total of 151 adult COVID-19 patients. Pooled analysis revealed that serum KL-6 was significantly higher in severe patients (SMD = 1.16; 95% CI = 0.69–1.63) with moderately high pooled sensitivity (79%; 95% CI = 61–91%) and specificity (86%; 95% CI = 72–95%). Conclusion: High serum KL-6 may depict more severe lung injury in COVID-19 patients with moderately high sensitivity and specificity.

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)0.015
metaresearch head score (Gemma)0.131
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.693
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.131
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0160.008
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.162
GPT teacher head0.505
Teacher spread0.343 · 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 designMeta-analysis
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

Citations16
Published2021
Admission routes1
Has abstractyes

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