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Record W2405135019

Effective analgesia and decreased length of stay for patients undergoing radical prostatectomy: Effectiveness of a clinical pathway.

2006· article· en· W2405135019 on OpenAlexaff
McLellan Ra, David Bell, Rendon Ra

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsMedicineProstatectomyPatient satisfactionCohortNarcoticAcetaminophenComplicationSurgeryRadical retropubic prostatectomyAnesthesiaInternal medicineProstate cancerCancer
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the impact of a clinical pathway (CP) on length of stay (LOS), complications, readmission rates, and patient satisfaction for patients undergoing a radical retropubic prostatectomy (RRP). MATERIALS AND METHODS: A standardized CP for all patients undergoing RRP was developed and implemented. Post-operatively, patients enrolled in the CP received oral ibuprofen and acetaminophen analgesia, with oral and subcutaneous narcotics available for breakthrough pain. Patients enrolled in the CP were compared to a pre-CP historical cohort. Patients were asked to complete a short, validated satisfaction questionnaire 10 days post-operatively. RESULTS: Sixty-eight consecutive patients underwent a RRP following CP implementation and were compared to a historical cohort of 147 pre-CP patients. Median LOS decreased by 50% (4 days versus 2 days, p < 0.0001) while complication and readmission rates were unchanged. Patient satisfaction was high in all domains. Overall, 29.4% of patients treated within the CP required no narcotic analgesia during their admission. CONCLUSIONS: The implementation of a CP for patients undergoing a RRP is a simple and effective method for reducing LOS without compromising complication, readmission rates or patient satisfaction.

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)0.004
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.018
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 integrity0.0000.000
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 head0.077
GPT teacher head0.402
Teacher spread0.325 · 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.

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

Citations4
Published2006
Admission routes1
Has abstractyes

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