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Record W159237817

Use of tissue sealant for day surgery parotidectomy.

2008· article· en· W159237817 on OpenAlexaffabout
Peter Conboy, Dale H. Brown

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicThyroid and Parathyroid Surgery
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsGynecologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the use of tissue sealant in facilitating day surgery parotidectomy without the use of surgical drains and to consider the potential economic benefit using this technique. STUDY DESIGN AND SETTING: Prospective cohort study of 21 patients undergoing parotidectomy for nonmalignant disease in a university hospital. Surgery as a day procedure without the use of surgical drains was planned. The costs associated with parotidectomy, including the use of tissue sealant and its delivery system, versus in-patient admission with a drain were calculated and compared. METHODS AND OUTCOME MEASURES: Parotidectomy was undertaken by one surgeon. Prior to wound closure, the skin flap and wound bed were approximated using Tisseel tissue sealant (Baxter Corp., Mississauga, ON). Data regarding the costs of the tissue sealant, the delivery system, and hospital in-patient stay were obtained to enable an economic comparison. Patients were followed to assess surgical outcome and document any complications. RESULTS: There were no major surgical complications. One patient required admission for control of postoperative nausea. None of the patients felt that discharge had been premature. The estimated cost advantage of this technique applied to institutions in Canada was $1,775 per case. CONCLUSIONS: Parotidectomy can be undertaken safely in a day surgery setting without the need for surgical drains. The increased cost associated with the use of tissue sealant compared with surgical drains is greatly overshadowed by the economic advantage of undertaking day surgery. There is a significant potential cost saving to the health care system.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.258
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.129
GPT teacher head0.263
Teacher spread0.134 · 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

Citations25
Published2008
Admission routes2
Has abstractyes

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