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Record W7007864795

Alexithymia as a Predictor of Chronic Tension Headaches

2017· other· en· W7007864795 on OpenAlexaboutno aff

Bibliographic record

Venuezvestiya of the National Academy of Sciences of Belarus (National Academy of Sciences of Belarus) · 2017
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaTension headacheMigraineHeadachesDepression (economics)AnxietyFeelingBeck Depression InventoryChronic MigraineNeuroticism
DOInot available

Abstract

fetched live from OpenAlex

© 2016, Springer Science+Business Media New York.Alexithymia as a violation of inter-hemispheric communication which has no visible organic brain changes is now regarded as a predictor of many chronic physical and neurological diseases, but it has not yet been regarded in connection with chronic tension headaches. The authors examined 137 people with tension-type headache (33 men, 84 women) aged 30–50 years (average age 40,75 ± 6,29) in order to clarify a link to alexithymia. The diagnosis of tension headache was conducted according to the International classification of headaches, 3rd edition (beta version). The authors used the original headache diary, Toronto alexithymia scale, Hospital anxiety and depression scale, the measurement of the space under the curve of headache. It was found that the patients with alexithymia have difficulty in describing the place of headaches (the average number of word descriptors at most 1 word), distrust of doctors, abuse of analgesics and tend to use alternative medicine methods. The intensity (p = 0.0001) and the frequency of headaches (p = 0.0028) is significantly higher in patients with alexithymia, and the more common are depression (p = 0.042) and impaired nocturnal sleep (p = 0.001). Patients with chronic tension-type headache associated with sleep disorders, adaptation and symptoms of depression should be considered as alexithymic personalities until the contrary is proved. Physicians should be aware of the fact that alexithymic patients have problems with feelings verbalization, and if patient’s complaints are vague a doctor should use questionnaires, words-descriptors, phrases-descriptors and other auxiliary verbal techniques for accurate diagnosis.

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)0.012
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.038
Scholarly communication0.0000.002
Open science0.0110.002
Research integrity0.0020.001
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 head0.068
GPT teacher head0.350
Teacher spread0.282 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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