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Record W2894988819 · doi:10.1111/jcpe.13015

The antidepressant drug, sertraline, hinders bone healing and osseointegration in rats’ tibiae

2018· article· en· W2894988819 on OpenAlexafffund
Lina Abu Nada, Ahmed Al Subaie, Alaa Mansour, Xixi Wu, Mohamed‐Nur Abdallah, Haider Al‐Waeli, Ala’ Ersheidat, Laura S. Stone, Monzur Murshed, Faleh Tamimi

Bibliographic record

VenueJournal Of Clinical Periodontology · 2018
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsShriners Hospitals for Children - CanadaUniversity of TorontoMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsOsseointegrationSertralineBone healingTibiaMedicineDentistryImplantAntidepressantInternal medicineSurgery

Abstract

fetched live from OpenAlex

AIM: Selective serotonin reuptake inhibitors (SSRIs) are one of the most common antidepressant drugs. SSRI use is associated with increased risk of bone fracture and titanium implant failure. The aim of this in vivo study was to investigate the effect of SSRIs on osseointegration and bone healing. MATERIALS AND METHODS: On a total of 24 Sprague-Dawley rats, a custom-made titanium implant was placed in the left tibia, while a unicortical defect was created in the right tibia. Rats were assigned randomly into two groups and received a daily dose of either sertraline (5 mg/kg) or saline. After two weeks, they were euthanized and bone healing and osseointegration were assessed by micro-CT and histology. RESULTS: Bone formation in bone defects was significantly lower (p < 0.05) in sertraline-treated rats (BV/TV = 20.67 ± 11.98%) compared to the controls (BV/TV = 37.87 ± 9.56%). Furthermore, the percentage of osseointegration was significantly lower (p < 0.05) in sertraline-treated rats (34.40 ± 7.17%) compared to the controls (54.37 ± 8.58%). CONCLUSION: Sertraline hinders bone healing and implant osseointegration.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.090
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.072
GPT teacher head0.453
Teacher spread0.381 · 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.

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

Citations32
Published2018
Admission routes2
Has abstractyes

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