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Record W3043933082 · doi:10.1186/s40463-020-00431-8

Impact of ENT resource nurses in improving operating room efficiency

2020· article· en· W3043933082 on OpenAlexaff
Kaishan Aravinthan, Connor Holmes, Sreejit Parameswaran Nair, Anil R. Sharma, Russell Murphy

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsResource (disambiguation)BusinessComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Operating room (OR) efficiency is related to minutes spared from surgical time and has been linked to the make up of surgical teams and operating room workplace. The research on the efficiency of surgical nursing staff members is scant. The current study evaluates the effect of ENT trained OR resource nurses on the efficiency of operating time during ENT procedures. METHODS: Five hundred seventy-three ENT surgery cases from 4 surgeons were retrospectively reviewed. Two hundred forty-two cases had ENT OR nursing staff and 331 cases had non-ENT OR nursing staff. Requested operative times (ROT) and true operative times (TOT) were analyzed. The difference between the TOT and ROT was used to measure operating time efficiency. RESULTS: Cases with ROT < 30 min (M = -1.19, SD = 5.01) required 3.34 min less than planned for when an ENT nurse was present compared to those with non-ENT nursing staff which required on average 2.15 min (M = 2.15, SD = 5.68) longer than ROT. Furthermore, cases with ROT > 30 min (M = -4.32, SD = 10.85) required 10.85 min less than planned for when an ENT nurse was present. Conversely with non-ENT nursing staff cases with a ROT > 30 min required on average 6.53 min (M = 6.53, SD = 11.85) longer than ROT. CONCLUSION: ENT resource nurses were shown to improve OR efficiency in cases less than 30 min and greater than 30 min. Cases that were greater than 30 min showed the largest increase in efficiency. Specialized ENT nursing staff improved efficiency during common ENT surgeries.

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)0.002
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 score0.014
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.050
GPT teacher head0.381
Teacher spread0.330 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Otolaryngology - Head and Neck SurgerySame topicHealthcare Operations and Scheduling OptimizationFrench-language works237,207