MétaCan
Menu
← Back to cohort

The impact of vitamin D supplementation on peripheral neuropathy in a sample of Egyptian prediabetic individuals

2021· dataset· en· W4394373988 on OpenAlexaboutno aff
Mohamed Reda Halawa, Iman Ahmed, Nahla Fawzy Abouelezz, Nagwa Roushdy Mohamed, Naira Hany Abdelaziz Khalil, Laila Mahmoud Ali Hendawy

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPeripheral neuropathySample (material)Internal medicineMedicinePeripheralVitaminDiabetes mellitusEndocrinologyChemistry

Abstract

fetched live from OpenAlex

short-form McGill Pain Questionnaire:to assess the dimensions of pain including pain sensations such as throbbing, shooting, stabbing, sharp, cramping, gnawing, hot, burning, aching, heavy, tender, and splitting. Descriptors l-11 represents the sensory dimension of pain; which encompasses both the quality and severity of pain. It includes the patient's report of the location, quality, and intensity of pain. Assessing this dimension helps quantify the pain and clarify the extent of poorly localized or radiating pain and 12-15 represent the affective dimension which describes the unpleasantness, the pain feels like a single unpleasant bodily experience. Each descriptor was ranked on an intensity scale of 0 = none, 1 = mild, 2 = moderate, 3 = severe. Interpretation: The more the score the more pain Douleur Neuropathic 4 diagnostique questionnaire (DN4) : to assess symptoms reflecting pain as sensations of burning, painful cold, electric shocks, tingling, pins and needles, and brushing Interpretation: If the patients score is ≥ 4, the test indicates that the patient is likely to be suffering from neuropathic pain

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)0.001
metaresearch head score (Gemma)0.005
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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
GenreDataset

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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFigshare→Same topicPain Mechanisms and Treatments→French-language works237,207→