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Record W7046564104

The economics of enhanced recovery

2015· dissertation· en· W7046564104 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéMcGill University
KeywordsPsychological interventionConstruct validityColorectal surgeryRandomized controlled trialEsophagectomyProspective cohort studyClinical trialMEDLINECohort studyElective surgery
DOInot available

Abstract

fetched live from OpenAlex

Enhanced recovery after surgery (ERAS) pathways are multidisciplinary clinical care pathways incorporating multiple evidence-based interventions designed to decrease the surgical stress response, enhance recovery, and improve outcomes. Multiple randomized trials have demonstrated the clinical effectiveness of ERAS over conventional care for elective colorectal surgery, but these pathways require significant resources to design, implement, and maintain. There is little economic evidence to support ERAS, as the existing data are low quality and there are large knowledge gaps regarding post-discharge outcomes and the socioeconomic impact of ERAS. Therefore the objective was to determine the cost-effectiveness of ERAS versus conventional care for patients undergoing elective colorectal surgery.In order to adequately measure recovery, the postoperative recovery construct was conceptually defined as a multidimensional construct that followed an expected trajectory of immediate postoperative deterioration and then a gradual rehabilitation back to or surpassing preoperative baseline. This definition was used to validate the SF-6D, a multi-attribute utility instrument, as a measure of postoperative recovery and for use as the main outcome measure of the cost-effectiveness analysis. Superior validity evidence was also provided for the SF-6D over the EQ-5D, another utility instrument. A pilot study as performed to estimate the cost impact of ERAS for esophagectomy using deviation-based cost-modeling, a novel method to analyze costs and outcomes for clinical pathways. Results from this pilot study were then used for sample size calculations for the cost-effectiveness analysis comparing ERAS and conventional care for colorectal surgery.The main study was a multi-institutional prospective cohort study that recruited adult patients undergoing elective colorectal surgery over a one-year period (10/2012 to 10/2013). One centre utilized ERAS routinely and the other did not. Costs and outcomes were measured over a 60-day time horizon. A total of 190 patients (95 ERAS, 95 conventional care) participated. ERAS was associated with lower length of hospitalization, less productivity loss, less caregiver burden, and decreased outpatient resource utilization. ERAS was also associated with decreased costs from a societal perspective (mean difference -2985 CAN$, 95% CI -5753, -373), but no difference in quality-adjusted life (mean difference: +0.87 quality-adjusted days, 95% CI -1.23, 2.97) compared to conventional care. Uncertainty analysis reported that ERAS was highly probable (>98% at all willingness-to-pay thresholds) to be cost-effective. The base-case results were insensitive to multiple sensitivity scenarios and subgroup analyses. In conclusion, evidence was provided to support the cost-effectiveness of ERAS over conventional care for patients undergoing elective colorectal surgery. In particular, the analysis addressed many of the limitations of previous economic evaluations and used a validated measure for postoperative recovery as the main outcome measure. Future research should focus on the costs and benefits of ERAS on a population level. High value cost-effective healthcare can be obtained through ERAS, as it lowers costs without compromising outcomes.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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