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Record W2891690058 · doi:10.1016/s0140-6736(18)31644-1

Treatment correlates of successful outcomes in pulmonary multidrug-resistant tuberculosis: an individual patient data meta-analysis

2018· review· en· W2891690058 on OpenAlexafffund
Nafees Ahmad, Shama D. Ahuja, Onno W. Akkerman, Jan‐Willem C. Alffenaar, Laura Anderson, Parvaneh Baghaei, Didi Bang, Pennan M. Barry, Mayara Lisboa Bastos, Digamber Behera, Andrea Benedetti, Gregory P. Bisson, Martin J. Boeree, Maryline Bonnet, Sarah K. Brode, James C. M. Brust, Ying Cai, Éric Caumes, J. Peter Cegielski, Rosella Centis, Pei‐Chun Chan, Edward D. Chan, Kwok Chiu Chang, Macarthur Charles, Andra Cīrule, Margareth Pretti Dalcolmo, Lia D’Ambrosio, Gèrard de Vries, Keertan Dheda, Aliasgar Esmail, Jennifer Flood, Gregory J. Fox, M. Jachym, Geisa Fregona, Regina Gayoso, Medea Gegia, Maria Tarcela Gler, Sue Gu, Lorenzo Guglielmetti, Timothy H. Holtz, Jennifer Hughes, Petros Isaakidis, Leah G. Jarlsberg, Russell R. Kempker, Salmaan Keshavjee, Faiz Ahmad Khan, Maia Kipiani, Serena P. Koenig, Won‐Jung Koh, Afrânio Lineu Kritski, Līga Kukša, Charlotte Kvasnovsky, Nakwon Kwak, Zhiyi Lan, Christoph Lange, Rafael Laniado-Laborı́n, Myungsun Lee, Vaira Leimane, Chi‐Chiu Leung, Eric Chung-Ching Leung, Pei Zhi Li, Phil Lowenthal, Ethel Leonor Nóia Maciel, Suzanne M. Marks, Sundari Mase, Lawrence Mbuagbaw, Giovanni Battista Migliori, Vladimir Milanov, Ann C. Miller, Carole D. Mitnick, Chawangwa Modongo, Erika Mohr-Holland, Payam Nahid, Norbert Ndjeka, Max R. O’Donnell, Nesri Padayatchi, Domingo Palmero, Jean W. Pape, Laura Jean Podewils, Ian S. Reynolds, Vija Riekstiņa, J. Robert, M. J. Rodríguez, Barbara Seaworth, Kwonjune J. Seung, Kathryn Schnippel, Tae Sun Shim, Rupak Singla, Sarah E. Smith, Giovanni Sotgiu, Ganzaya Sukhbaatar, Payam Tabarsi, Simon Tiberi, Anete Trajman, Lisa Trieu, Zarir Udwadia, Tjip S. van der Werf, Nicolas Véziris, Piret Viiklepp, Stalz Charles Vilbrun, Kathleen F. Walsh, Janice Westenhouse, Wing-Wai Yew, Jae‐Joon Yim, Nicola M. Zetola, Matteo Zignol, Dick Menzies

Bibliographic record

VenueThe Lancet · 2018
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonImpactMcGill University Health CentreSinai Health SystemUniversity of TorontoUniversity Health NetworkWest Park Healthcare Centre
FundersEuropean and Developing Countries Clinical Trials PartnershipNational Institute of Allergy and Infectious DiseasesMedical Research CouncilCenters for Disease Control and PreventionOtsuka PharmaceuticalEuropean Respiratory SocietyEuropean CommissionAmerican Thoracic SocietyFogarty International CenterJanssen PharmaceuticalsGilead SciencesInsmedCanadian Institutes of Health Research
KeywordsMedicineInternal medicineTuberculosisMeta-analysisOdds ratioCochrane LibraryObservational studyMEDLINEExtensively drug-resistant tuberculosisLevofloxacinIntensive care medicineMycobacterium tuberculosisAntibioticsPathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.033
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.292
GPT teacher head0.445
Teacher spread0.153 · 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.

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

Citations639
Published2018
Admission routes2
Has abstractno

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