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Record W4293102215 · doi:10.1371/journal.pone.0273580

Pediatric Clinical Classification System for use in Canadian inpatient settings

2022· article· en· W4293102215 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenuePLoS ONE · 2022
Typearticle
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersHospital for Sick ChildrenPhysicians' Services Incorporated Foundation
KeywordsMedicineMEDLINEBiology

Abstract

fetched live from OpenAlex

BACKGROUND: A classification system that categorizes International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes into clinically meaningful categories is important for pediatric clinical and health services research using administrative data. While a Pediatric Clinical Classification System (PECCS) is available for the United States ICD-10 system (i.e, ICD-10-CM), differences in the ICD-10 system between countries limits PECCS use in Canada. OBJECTIVE: To translate PECCS from ICD-10-CM to ICD-10-CA for use in Canada (PECCS-CA), and examine the utility of PECCS-CA in administrative data of pediatric hospital encounters in Ontario, Canada. METHODS: PECCS was translated by mapping each ICD-10-CA code to its corresponding ICD-10-CM code, based on code description and alphanumeric code, using automated functions in Microsoft Excel. All unmatched ICD-10-CA codes were manually matched to an ICD-10-CM code. The ICD-10-CA codes were mapped to a PECCS category based on the placement of the corresponding ICD-10-CM code. Finally, in this cross-sectional study, the utility of PECCS-CA was examined in pediatric hospital encounters in children <18 years of age with an inpatient or same day surgery encounter, between April 1, 2014 to March 31, 2019 in Ontario. RESULTS: In total, 16,992 ICD-10-CA diagnosis codes were mapped to 781 mutually exclusive condition categories that included pediatric specific conditions and treatments in PECCS-CA. From the 781 categories, 777 (99.5%) were derived from the original PECCS, 3 (0.4%) from merging the original PECCS categories, and 1 (0.1%) was newly developed. The PECCS-CA was applied to health administrative data of 911,732 hospital encounters in children. The most prevalent condition in children was low birth weight (n = 54,100 encounters). CONCLUSION: The PECCS-CA is an open-source classification system which maps ICD-10-CA codes into 781 clinically important pediatric categories. The PECCS-CA can be used for pediatric health services and outcomes research in Canada.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.563
GPT teacher head0.457
Teacher spread0.106 · 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