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Record W2153689608 · doi:10.1159/000362108

Treatment Policy rather than Patient Characteristics Determines Convection Volume in Online Post-Dilution Hemodiafiltration

2014· article· en· W2153689608 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBlood Purification · 2014
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsHôpital Saint-LucCentre Hospitalier de l’Université de Montréal
FundersZonMwNierstichtingRoche NederlandFresenius Medical Care North America
KeywordsHematocritMedicineBlood volumeInternal medicineConvectionCardiologyUrologyMechanicsPhysics

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: Sub-analyses of three large trials showed that hemodiafiltration (HDF) patients who achieved the highest convection volumes had the lowest mortality risk. The aims of this study were (1) to identify determinants of convection volume and (2) to assess whether differences exist between patients achieving high and low volumes. METHODS: HDF patients from the CONvective TRAnsport STudy (CONTRAST) with a complete dataset at 6 months (314 out of a total of 358) were included in this post hoc analysis. Determinants of convection volume were identified by regression analysis. RESULTS: Treatment time, blood flow rate, dialysis vintage, serum albumin and hematocrit were independently related. Neither vascular access nor dialyzer characteristics showed any relation with convection volume. Except for some variation in body size, patient characteristics did not differ across tertiles of convection volume. CONCLUSION: Treatment time and blood flow rate are major determinants of convection volume. Hence, its magnitude depends on center policy rather than individualized patient prescription.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.245
Teacher spread0.235 · 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