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Record W4416982206 · doi:10.21203/rs.3.rs-8197118/v1

Problem-Oriented Electronic Health Records and Patient Clinical Outcomes: a Scoping Review

2025· preprint· W4416982206 on OpenAlexaff
Sıdıka Ece Yokuş, Sandra León-Herrera, Henrique Teruo Arai, Mariela Zaros Galana, Vinícius Jobim Fischer, Ileana Gefaell Larrondo, M.E. Sánchez, Inês A. C. Pereira, Edmond Li, Maria Angeliki S. Pavlou, Lucas Magalhães Moreira, Mark P. de Souza, Lucas Bastos Marcondes Machado, Nicolle Taissun, A. S. Oliveira, János Valery Gyuricza, Ana Luísa Neves, P. P. Gupta, Steven van de Vijver, Jan De Maeseneer, Kees van Boven, Raquel Gómez Bravo, Luis Fernandez Lopez

Bibliographic record

VenueResearch Square · 2025
Typepreprint
Language
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsWestern University
Fundersnot available
KeywordsMedical recordReferralHealth careObservational studyHealth recordsMEDLINEPublic healthDisease

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)0.039
metaresearch head score (Gemma)0.166
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.166
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0050.005
Bibliometrics0.0200.022
Science and technology studies0.0010.002
Scholarly communication0.0080.005
Open science0.0020.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.217
GPT teacher head0.613
Teacher spread0.396 · 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 designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractno

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