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Record W4321612488

Food cravings during the first week of concussion.

2022· article· en· W4321612488 on OpenAlexaffabout
Mohsen Kazemi, Sarah S. Donaldson, Melissa Hamilton, Nikolaus Suich

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsConcussionIrritabilityMedicinePediatricsPsychiatryPoison controlInjury preventionAnxiety
DOInot available

Abstract

fetched live from OpenAlex

The brain utilizes glucose as its main source of energy. Traumatic brain injuries may alter the brain's ability to shuttle glucose effectively; therefore, the symptoms experienced may be a signal of the dysregulation. The objective of this cross-sectional study was to investigate the presence of any specific food cravings during the first week post-concussion and if the consumption of such a food decreased the symptoms of concussion. The link to the survey was posted on 4 Canadian organization websites from November 2020 to February 2021. Any individual over 18 years old who had suffered one of more concussions in the past 12 months was included. 73 females and 24 males, the majority aged 18-40 years, completed the survey. Participants with combined carbohydrate and sweet cravings reported significantly more symptoms of increased emotions (p=0.04), irritability (p=0.03), sadness (p=0.04), nervousness (p=0.03), and sleep disturbances (p=0.05) than those without these cravings. Consumption of the craved food did not change the concussion symptoms.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.266
Teacher spread0.207 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2022
Admission routes2
Has abstractyes

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Same venuePubMed→Same topicTraumatic Brain Injury Research→French-language works237,207→