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Record W2865494915 · doi:10.1016/j.ekir.2018.07.007

Newer Point-of-Care Biosensors Are Expected to Permit Early Detection and Better Management of Acute and CKD

2018· editorial· en· W2865494915 on OpenAlexaboutno aff
Jorge Cerdá

Bibliographic record

VenueKidney International Reports · 2018
Typeeditorial
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineScopusKidney diseaseContext (archaeology)PopulationAcute kidney injuryIntensive care medicineHealth careMEDLINEFamily medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

For any intervention in the public health of large populations to be effective, accurate knowledge of the incidence and prevalence of acute and chronic diseases is a basic requirement. Recognition of a disease and its severity is a multistaged process, but its implementation is dependent not only on clinical suspicion, confirmatory measures, and a final diagnosis, but also on modifiers, such as awareness of the condition, the diagnostic context, and the resources available to implement confirmatory studies (Figure 1).

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)0.013
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.057
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0060.006
Open science0.0040.002
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0570.024

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 head0.008
GPT teacher head0.310
Teacher spread0.301 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2018
Admission routes1
Has abstractyes

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