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Record W4283526870 · doi:10.1177/13674935221109683

Reported costs of children with medical complexity—A systematic review

2022· review· en· W4283526870 on OpenAlexaff
Michael Sidra, Meghan Sebastianski, Arto Öhinmaa, Sholeh Rahman

Bibliographic record

VenueJournal of Child Health Care · 2022
Typereview
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsActivity-based costingMedicineHealth carePopulationPerspective (graphical)MEDLINEActuarial scienceOperations managementBusinessNursingFamily medicineEnvironmental healthMarketingComputer scienceEconomics

Abstract

fetched live from OpenAlex

Examining reported costs for Children with Medical Complexity (CMCs) is essential because costing and resource utilization studies influence policy and operational decisions. Our objectives were to (1) examine how authors identified CMCs in administrative databases, (2) compare reported costs for the CMC population in different study settings, and (3) analyze author recommendations related to reported costs. We undertook a systematic search of the following databases: Medical Literature Analysis and Retrieval System Online, Excerpta Medica dataBase, Cumulative Index to Nursing and Allied Health Literature, and Cochrane Library with a focus on CMCs as a heterogeneous group. The most common method used n = 11 (41%) to identify the CMC population in administrative data was the Complex Chronic Conditions methodology. The majority of included studies reported on health care service costs n = 24 (89%). Only n = 3 (11%) of the studies included costs from the family perspective. Author recommendations included standardizing how costs are reported and including the family perspective when making care delivery or policy decisions. Health system administrators and policymakers must consider the limitations of reported costs when assessing local costing studies or comparing costs across jurisdictions.

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 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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.126
GPT teacher head0.378
Teacher spread0.251 · 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 teacher head, not a consensus.

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

Citations15
Published2022
Admission routes1
Has abstractyes

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