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Record W4402554716 · doi:10.1177/21925682241286451

Waterpipe Smoking and Lumbar Intervertebral Disc Degeneration: A Pilot Study

2024· article· en· W4402554716 on OpenAlexaff
Hala Ishak, Tarek Sunna, Sara Assaf, Hanin Banna, Riad Khouzami, Zhi Wang, Ghazi Zaatari, Diana Rahme, Carine J. Sakr

Bibliographic record

VenueGlobal Spine Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineLumbarIntervertebral discRetrospective cohort studyMagnetic resonance imagingDegeneration (medical)Significant differenceCigarette smokingSurgeryInternal medicinePathologyRadiology

Abstract

fetched live from OpenAlex

Study Design Retrospective study. Objective The objective of this study is to investigate the association of waterpipe smoking with lumbar intervertebral disc degeneration (IVDD). Methods This is a retrospective chart review study. A total of 286 adults who underwent a lumbar magnetic resonance imaging (MRI) at a tertiary medical center were included and divided into three groups. Group 1 (n = 125) included non-smokers, group 2 (n = 80) smoked cigarettes only, and group 3 (n = 81) smoked waterpipe only. The intervertebral discs were graded using the Pfirmann disc degeneration grading system. Results The study showed higher lumbar disc degeneration scores for waterpipe and cigarette smokers compared to non-smokers at all spinal levels. Specifically, post hoc analysis showed that there was a significant difference at L1-L2 between cigarette smokers and non-smokers ( P = 0.007) and between waterpipe smokers and non-smokers ( P = 0.013), and a significant difference at L3-L4 and L4-L5 between non-smokers and cigarettes smokers ( P < .001 and P = .029 respectively). Conclusion Waterpipe smoking is associated with lumbar intervertebral disc degeneration.

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)0.002
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.329
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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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