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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

Tossaporn Sapsitthikul, On behalf of Thailand PDOPPS Steering Committee, Krit Pongpirul, On behalf of Thailand PDOPPS Steering Committee, Talerngsak Kanjan

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.047
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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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