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Iron status of children with attention deficit/hyperactivity disorder: A systematic review

2019· review· en· W4312853547 on OpenAlexaboutno aff
Alexia Degremont, Gladys O. Latunde‐Dada, Dr Elena Philippou, Rishika Jain

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsAttention deficit hyperactivity disorderAttention deficit disorderAttention deficitIron statusPsychologyPsychiatryClinical psychologyIron deficiency

Abstract

fetched live from OpenAlex

Currently, there is little known about the patient experience in traveling, transitioning, or relocating for other types of medical care to more urban centres, especially in the context of medical care transfers from rural and/or isolated areas of Northwestern British Columbia. This patient-oriented study is purposefully including patient partners in the steering committee to ensure a higher level of knowledge translation and to help inform decisions with their insights and awareness. The engagement of patient partners allows the study to focus on their priorities which, in turn, will lead to more prioritized findings and better outcomes for the patients, patient families, and health care providers. At the most recent steering committee meeting, patient partners were included in decisions being made for the scoping literature review. The group reviewed the initial search terms to establish an all-encompassing search. The three main questions being brought to the group were: u201cwhat ages should be included?u201d, u201cdate range?u201d, and u201cwhat countries should be included?u201d. The group decided that we would focus on ages 19+ due to children with critical illness having different resources that meant this topic may need to be researched separately. The dates decided on would look at the last 10 years, 2009-2019, as many felt that the research before this will likely not be relevant due to the vast advancements in technology, cellphones and 3G networks being one example. Thirdly, while the US may have differences in medical insurance, they also have more published research than Canada, so the group ultimately decided to include the US as well as additional countries in the scoping literature review. These are only a few examples of how our patient partners provided us with helpful guidance that we may not have otherwise considered and how they will continue to enlighten the research process with their own rich experiences.

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)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.348
Teacher spread0.304 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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