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Record W4403402522 · doi:10.1111/apa.17458

Early identification and communication in cerebral palsy: Navigating a collaborative approach for neonatal follow‐up programmes

2024· review· en· W4403402522 on OpenAlexafffund
Paige Church, Rudaina Banihani, Karen A. Thomas, Maureen Luther, Brenda Agnew, A Makino, Sophie Lam‐Damji, Darcy Fehlings

Bibliographic record

VenueActa Paediatrica · 2024
Typereview
Languageen
FieldMedicine
TopicInfant Development and Preterm Care
Canadian institutionsMcMaster Children's HospitalMcMaster UniversityUniversity of TorontoHolland Bloorview Kids Rehabilitation HospitalSunnybrook Health Science Centre
FundersNational Institute of General Medical SciencesMichael Smith Health Research BC
KeywordsMedicineTerminologyIdentification (biology)Cerebral palsyProcess (computing)Function (biology)Medical educationComputer sciencePsychiatryLinguistics

Abstract

fetched live from OpenAlex

AIM: This article will provide a clinical case demonstrating the implementation of early identification and review the tools and findings and the diagnostic approach. We will review highlighted literature on the subject of communicating a diagnosis. While improved function is a critical goal, the process of communicating the diagnosis of CP can be challenging for both parents and providers. It aims to provide insights on the evidence supporting early identification and discusses strategies for effective communication of crucial information. METHODS: The article reviews the literature on communication of a diagnosis. RESULTS: Thirteen articles were identified relating to the communication of a diagnosis of CP and parent experience. We examine this evidence, leveraging the knowledge of an interdisciplinary team and incorporating feedback from parents. CONCLUSION: Strategies for effective communication include engagement with families, community therapy teams and all medical providers. Consistent, individualised, collaborative communication is critical. Awareness of ableism and use of balanced, value-neutral terminology is recommended.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.329
Teacher spread0.300 · 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 designNot applicable
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

Citations3
Published2024
Admission routes2
Has abstractyes

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