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Record W2767502570 · doi:10.1002/hed.24934

Outpatient versus inpatient thyroidectomy: A systematic review and meta‐analysis

2017· review· en· W2767502570 on OpenAlexaffabout
Daniel J. Lee, Christopher J. Chin, Chris J. Hong, Stefan Perera, Ian Witterick

Bibliographic record

VenueHead & Neck · 2017
Typereview
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsSaint John Regional HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineThyroidectomyCochrane LibraryObservational studyPostoperative hematomaMEDLINERelative riskMeta-analysisConfidence intervalOutpatient surgerySurgeryEmergency medicineComplicationInternal medicineAmbulatoryThyroid

Abstract

fetched live from OpenAlex

BACKGROUND: Outpatient thyroidectomy has gained popularity due to improved resource utilization. METHODS: We conducted a systematic review and meta-analysis using MEDLINE, EMBASE, CINAHL, Web of Science, and the Cochrane library. We included all studies examining the outcomes of outpatient thyroidectomy as compared with those of inpatient thyroidectomy. Risk of bias was assessed using the Newcastle-Ottawa scale. Postoperative complications (hematoma, hypocalcemia, and recurrent laryngeal nerve injury) and readmission/reintervention rates were compared. RESULTS: After screening 1665 records, 10 nonrandomized observational studies were included. There were fewer complication rates in the outpatient group than the inpatient group (relative risk [RR] 0.56; 95% confidence interval [CI] 0.37-0.83). There was no difference in readmission/reintervention rates (RR 0.60; 95% CI 0.33-1.09). CONCLUSION: The results suggest outpatient thyroidectomy may be as safe as inpatient thyroidectomy in appropriately selected patients. The results are limited by high risk of bias. Well-designed prospective studies are necessary to further assess the safety of outpatient thyroidectomy.

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)0.009
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.025
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.254
GPT teacher head0.433
Teacher spread0.180 · 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.

Study designMeta-analysis
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

Citations75
Published2017
Admission routes2
Has abstractyes

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