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Record W4394757406 · doi:10.7759/cureus.58085

Cracking the Code of Digital Discomfort Through the Dynamic Fusion of Matrix Rhythm Therapy and Physiotherapy Exercises for Text Neck Syndrome

2024· article· en· W4394757406 on OpenAlexaff
Divya Gohil, Reena S Kathed, Tushar Palekar

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsRhythmPhysical therapyPhysical medicine and rehabilitationCode (set theory)MedicinePsychologyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

Text neck syndrome refers to the excessive use of electronic devices such as laptops, mobile phones, and so on, which causes prolonged and continued forward bending of the neck, leading to a strain in the muscle, causing muscle imbalance, and leading to poor posture. In this article, we focus on a case of a 22-year-old female who has a daily average screen time of around four to five hours, which leads to stress on cervical muscles that further develop into tightness and cause poor posture. She is managed with physiotherapy treatment that focuses on reducing pain and increasing the strength of the individual. The physiotherapy treatment focuses on the prevention of further damage to the cervical muscles and educating the individual to perform minimum forward bending by providing ergonomic advice, reducing pain, and improving range of motion.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.336
Teacher spread0.315 · 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 designNon-randomized trial
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
Published2024
Admission routes1
Has abstractyes

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