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Record W2147621422 · doi:10.3906/sag-0809-26

The relationship between daily caffeine consumption and withdrawal symptoms: a questionnaire-based study

2010· article· en· W2147621422 on OpenAlexaboutno aff
Nermin Küçer

Bibliographic record

VenueTURKISH JOURNAL OF MEDICAL SCIENCES · 2010
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
Fundersnot available
KeywordsCaffeineIrritabilityTurkishMedicineConsumption (sociology)PsychologyPhysical therapyPsychiatryAnxiety

Abstract

fetched live from OpenAlex

To estimate daily caffeine intake among a group of university students at the Kocaeli Vocational School of Health Services in Turkey, and to determine the relationship between daily caffeine consumption and withdrawal symptoms. Materials and methods: This survey study was conducted using a questionnaire that was administered to 156 university students (129 females, 27 males) at the Kocaeli Vocational School of Health Services in Kocaeli, Turkey. The quantity of caffeine-containing products consumed was recorded on a daily basis, depending on the dietary habits of the consumer. The t test for differences between 2 proportions (using the normal approximation) was used for comparison of the frequency of complaints that began within 12-24 h of the cessation of caffeine consumption in relation to the quantities of daily caffeine consumption at P < 0.05. Results: Daily caffeine intake was estimated to range from 0 to 500 mg day_{-1} A significant increase in headache, fatigue, irritability, and sleepiness/drowsiness (P < 0.05) was reported by student's whose daily caffeine consumption was > 200 mg than by those whose daily caffeine consumption was < 200 mg. Conclusion: The data obtained show that Turkish university students consume similar amounts of caffeine as American, Canadian, Swedish, and British university students. The survey results show that there was a relationship between daily caffeine consumption and withdrawal symptoms (headache, fatigue, irritability, and sleepiness/drowsiness).

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)0.007
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.071
GPT teacher head0.403
Teacher spread0.331 · 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 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

Citations10
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueTURKISH JOURNAL OF MEDICAL SCIENCESSame topicCoffee research and impactsFrench-language works237,207