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Record W2990471795 · doi:10.15173/mumj.v16i1.2021

Novel evidence depicting adverse long-term outcomes linked to tonsillectomy: a spotlight on overtreatment

2019· article· en· W2990471795 on OpenAlexaffabout
Kashyap Patel

Bibliographic record

VenueMcMaster University Medical Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicObstructive Sleep Apnea Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineIntensive care medicineTonsillectomyPsychological interventionAdverse effectQuality of life (healthcare)PerioperativeObstructive sleep apneaEvidence-based medicinePediatricsSurgeryAlternative medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Tonsillectomies (TEs) are the first line of treatment when patients present with recurrent tonsillitis, peritonsillar abscesses or obstructive sleep apnea. Though TEs have modest efficacy, they remain a common pediatric surgery in Canada. TEs are now viewed as a prophylactic measure used to prevent tonsil-related diseases. Simultaneously, there is a lack of evidence-based decision-making when recommending TEs, leading to overtreatment. Novel findings indicate that pediatric TE patients have an increased risk of complications and poor long-term outcomes including respiratory, infectious, and allergic disorders. A need for alternatives to TEs is evident; less invasive interventions with fewer perioperative complications and lifelong morbidities warrant further research. To prevent unnecessary adverse outcomes, healthcare providers should opt for more selective and evidence-based TE recommendations. Meanwhile, it is also imperative that physicians clearly communicate the potential quality of life implications associated with TEs. Healthcare and social mores surrounding TEs need to change towards a more evidence-based practice that focuses on improving patients’ quality of life. This commentary examines the current role of TEs, their long-term outcomes, and the implications of overtreatment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.048
GPT teacher head0.320
Teacher spread0.272 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes2
Has abstractyes

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