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Record W2093995765 · doi:10.1080/13638490310001649417

Remediation of attention deficits in children: a focus on childhood cancer, traumatic brain injury and attention deficit disorder

2004· review· en· W2093995765 on OpenAlexaff
Louise Penkman

Bibliographic record

VenuePediatric Rehabilitation · 2004
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsAlberta Children's Hospital
Fundersnot available
KeywordsTraumatic brain injuryAttention deficit disorderMedicineChildhood cancerPediatricsAttention deficitsPsychiatryPsychologyCancerCognition

Abstract

fetched live from OpenAlex

The purpose of this review is to examine the status of attention training in children. This body of literature is very small so the review examines available efficacy studies in three paediatric groups: children who have survived cancer affecting the central nervous system (CNS) or whose treatment has impacted the CNS, children with traumatic brain injury (TBI) and children with attention deficit disorder (ADD). Seven studies/case reports are reviewed. The results are encouraging, with six of seven describing some improvement on attention measures. An original case study is presented using Pay Attention! materials with a 6 year old survivor of acute lymphoblastic leukaemia (ALL). This represents only the third report of the use of attention training materials with a survivor of childhood cancer and the first case report of the use of these materials with a very young child (6 years of age).

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.331
Teacher spread0.311 · 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
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

Citations42
Published2004
Admission routes1
Has abstractyes

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