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Record W4396526950 · doi:10.1093/ckj/sfae136

Optimizing home visits through machine learning for preventing peritoneal dialysis-associated peritonitis: a proof of concept study and results from PDOPPS

2024· article· en· W4396526950 on OpenAlexaff
Tossaporn Sapsitthikul, Krit Pongpirul, Talerngsak Kanjanabuch, Piyatida Chuengsaman, Proadpran Punyabukkana, Ploy N. Pratanwanich, Panus Sawetpiyakul, Dhammika Leshan Wannigama, Paweena Susantitaphong, Natavudh Townamchai, Yingyos Avihingsanon, Jeffrey Perl, David W. Johnson, Roberto Pecoits‐Filho, Somchai Eiam‐Ong, Kriang Tungsanga, Jiruth Sriratanaban, Kearkiat Praditpornsilpa, Piyaporn Towannang, Kanittha Triamamornwooth, Pichet Lorvinitnun, Sirinart Raweewan, Jitta matawon, Nisa Thongbor, Suchai Sritippayawan, Nipa Aiyasanon, Guttiga Halue, Donkum Kaewboonsert, Pensri Uttayotha, Kittisak Tangjittrong, Wichai Sopassathit, Salakjit Pitakmongkol, Ussanee Poonvivatchaikarn, Bunpring Jaroenpattrawut, Somphon Buranaosot, Sukit Nilvarangkul, Warakoan Satitkan, Wanida Somboonsilp, Pimpong Wongtrakul, Ampai Tongpliw, Anocha Pullboon, Chanchana Boonyakrai, Montha Jankramol, Surapong Narenpitak, Piyarat Rojsanga, Apinya Wechpradit, Sajja Tatiyanupanwong, Chadarat Kleebchaiyaphum, Wadsamon Saikong, Worauma Panya, Siriwan Thaweekote, Sriphrae Uppamai, Jarubut Phisutrattanaporn, Sirirat Sirinual, Natchaporn Doenphai, Setthapon Panyatong, Puntapong Taruangsri, Boontita Prasertkul, Thanchanok Buanet, Rutchanee Chieochanthanakij, Panthira Passorn, Niwat Lounseng, Rujira Luksanaprom, Angsuwarin Wongpiang, Metinee Chaiwut, Worapot Treamtrakanpon, Ruchdaporn Phaichan, Peerapach Rattanasoonton, Wanlaya Thongsiw, Narumon Lukrat, Sayumporn Thaitrong, Phichit Songviriyavithaya, Yupha Laoong, Niparat Pikul, Uraiwan Parinyasiri, Korawee Sukmee, Hathairat kosing, Hataishanok Boonprasert, Napassaporn Semtapra, Tharaporn Sudjai, Piyarat kaewprasert, jarika homsang, Sudawan Iamngamsup, Pornpan Auamim, Nutchaprang Bumrungwat, Kandasud Sriudom, Sumalee Chankalee, Banphaeo-Charoenkrung

Bibliographic record

VenueClinical Kidney Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Michael's Hospital
FundersKidney Foundation of ThailandRoyal College of Physicians of ThailandThailand Science Research and InnovationChulalongkorn UniversityNational Science and Technology Development AgencyFaculty of Medicine, Chulalongkorn University
KeywordsPeritoneal dialysisPeritonitisProof of conceptMedicineDialysisIntensive care medicineInternal medicineComputer scienceOperating system

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 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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.363
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations3
Published2024
Admission routes1
Has abstractno

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