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Record W2099929136 · doi:10.3109/17518420903087558

Hitting a moving target: Basic mechanisms of recovery from acquired developmental brain injury

2009· review· en· W2099929136 on OpenAlexaff
Christopher C. Giza, Bryan Kolb, Neil G. Harris, Robert F. Asarnow, Mayumi L. Prins

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2009
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Lethbridge
FundersNational Institute of Neurological Disorders and Stroke
KeywordsNeuroplasticityTraumatic brain injuryNeuroscienceBrain damagePsychologyMedicineBrain developmentPhysical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

Acquired brain injuries represent a major cause of disability in the pediatric population. Understanding responses to developmental acquired brain injuries requires knowledge of the neurobiology of normal development, age-at-injury effects and experience-dependent neuroplasticity. In the developing brain, full recovery cannot be considered as a return to the premorbid baseline, since ongoing maturation means that cerebral functioning in normal individuals will continue to advance. Thus, the recovering immature brain has to 'hit a moving target' to achieve full functional recovery, defined as parity with age-matched uninjured peers. This review will discuss the consequences of developmental injuries such as focal lesions, diffuse hypoxia and traumatic brain injury (TBI). Underlying cellular and physiological mechanisms relevant to age-at-injury effects will be described in considerable detail, including but not limited to alterations in neurotransmission, connectivity/network functioning, the extracellular matrix, response to oxidative stress and changes in cerebral metabolism. Finally, mechanisms of experience-dependent plasticity will be reviewed in conjunction with their effects on neural repair and recovery.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.032
GPT teacher head0.292
Teacher spread0.260 · 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

Citations74
Published2009
Admission routes1
Has abstractyes

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