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Record W2012864628 · doi:10.2196/ijmr.3160

The Impact of Different Surgical Modalities for Hysterectomy on Satisfaction and Patient Reported Outcomes

2014· article· en· W2012864628 on OpenAlexvenueno aff
Michael C. Pitter, Christopher Simmonds, Usha Seshadri‐Kreaden, Helen B. Hubert

Bibliographic record

VenueInteractive Journal of Medical Research · 2014
Typearticle
Languageen
FieldMedicine
TopicUterine Myomas and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsModalitiesHysterectomyPatient satisfactionMedicineSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: There is an ongoing debate regarding the cost-benefit of different surgical modalities for hysterectomy. Studies have relied primarily on evaluation of clinical outcomes and medical expenses. Thus, a paucity of information on patient-reported outcomes including satisfaction, recovery, and recommendations exists. OBJECTIVE: The objective of this study was to identify differences in patient satisfaction and recommendations by approach to a hysterectomy. METHODS: We recruited a large, geographically diverse group of women who were members of an online hysterectomy support community. US women who had undergone a benign hysterectomy formed this retrospective study cohort. Self-reported characteristics and experiences were compared by surgical modality using chi-square tests. Outcomes over time were assessed with the Jonkheere-Terpstra trend test. Logistic regression identified independent predictors of patient satisfaction and recommendations. RESULTS: There were 6262 women who met the study criteria; 41.74% (2614/6262) underwent an abdominal hysterectomy, 10.64% (666/6262) were vaginal, 27.42% (1717/6262) laparoscopic, 18.94% (1186/6262) robotic, and 1.26% (79/6262) single-incision laparoscopic. Most women were at least college educated (56.37%, 3530/6262), and identified as white, non-Hispanic (83.17%, 5208/6262). Abdominal hysterectomy rates decreased from 68.2% (152/223) to 24.4% (75/307), and minimally invasive surgeries increased from 31.8% (71/223) to 75.6% (232/307) between 2001 or prior years and 2013 (P<.001 all trends). Trends in overall patient satisfaction and recommendations showed significant improvement over time (P<.001).There were differences across the surgical modalities in all patient-reported experiences (ie, satisfaction, time to walking, driving and working, and whether patients would recommend or use the same technique again; P<.001). Significantly better outcomes were evident among women who had vaginal, laparoscopic, and robotic procedures than among those who had an abdominal procedure. However, robotic surgery was the only approach that was an independent predictor of better patient experience; these patients were more satisfied overall (odds ratio [OR] 1.31, 95% CI 1.13-1.51) and on six other satisfaction measures, and more likely to recommend (OR 1.64, 95% CI 1.39-1.94) and choose the same modality again (OR 2.07, 95% CI 1.67-2.57). Abdominal hysterectomy patients were more dissatisfied with outcomes after surgery and less likely to recommend (OR 0.36, 95% CI 0.31-0.40) or choose the same technique again (OR 0.29, 95% CI 0.25-0.33). Quicker return to normal activities and surgery after 2007 also were independently associated with better overall satisfaction, willingness to recommend, and to choose the same surgery again. CONCLUSIONS: Consistent with other US data, laparoscopic and robotic hysterectomy rates increased over time, with a concomitant decline in abdominal hysterectomy. While inherent shortcomings of this retrospective Web-based study exist, findings show that patient experience was better for each of the major minimally invasive approaches than for abdominal hysterectomy. However, robotic-assisted hysterectomy was the only modality that independently predicted greater satisfaction and willingness to recommend and have the same procedure again.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.074
GPT teacher head0.477
Teacher spread0.402 · 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 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

Citations55
Published2014
Admission routes1
Has abstractyes

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