MétaCan
Menu
← Back to cohort
Record W2103324021 · doi:10.5489/cuaj.12220

Achieving the achievable in muscle-invasive bladder cancer

2012· article· en· W2103324021 on OpenAlexaffvenue
Chris Booth, W. Mackillop

Bibliographic record

VenueCanadian Urological Association Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsBladder cancerCancerMedicineUrologyComputer scienceCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Patient outcomes reported from clinical trials and case series from centres of excellence define the benchmark for what is achievable among patients with muscle-invasive bladder cancer (MIBC). However, because patients, medical care and health systems can be very different in routine clinical practice there is often a gap between efficacy (i.e., results observed in trials) and effectiveness (i.e., results observed in the general population).1,2 Population-based studies are important to identify gaps in care and areas for improvement so that clinicians and patients might move towards “achieving the achievable.”3 Multiple population-based studies and a meta-analysis have consistently demonstrated an inverse relationship between hospital cystectomy volume and postoperative mortality.4–8 However critical questions remain unanswered including: what factors are responsible for the observed volume effect?; how much of the observed effect relates to hospital volume versus individual surgeon volume?; and how should volume be defined, classified and analyzed? Furthermore, beyond operative mortality and complications there is considerably less literature describing the relationship between cystectomy volume and long-term survival.9 In the paper by Bianchi and colleagues published in this issue of CUAJ, the authors have evaluated the impact of hospital academic affiliation on short term radical cystectomy outcomes.10 Using records from the Health Care Utilization Project Nationwide Inpatient Sample the authors explore postoperative complications and mortality across hospitals in the United States. The unadjusted results suggest greater complication rates, length of stay (LOS), and post-operative mortality in patients who have surgery at non-academic hospitals. However, in the multivariate analysis the there is no difference in LOS and post-operative mortality and a statistically significant but clinically modest increase in complications. A more fundamental question is how to disentangle the relationship between hospital volume, academic status, and outcome? While most previous studies have analyzed volume as either a continuous variable or a categorical variable using tertiles/quartiles, Bianchi and colleagues dichotomize annual hospital caseload as greater than 15 or less than 15 cystectomies per year.10 The cut-point is very high relative to other studies where “high volume” hospitals are usually defined as those that perform >5 to 10 cystectomies per year.4–8 In dichotomizing this outcome and using such a high threshold, Bianchi and colleagues are left with only 12% (n = 1515) of their study population in the high volume group and all of these cases had surgery at academic hospitals. Accordingly it is very likely that any potential volume effect has been lost in the statistics. The authors suggest that patients treated at academic hospitals are slightly younger, have less comorbidity, and are more likely to have private health insurance.10 Despite adjusted analyses there remains the potential for unmeasured confounding. Higher volume hospitals might have higher volume surgeons with better surgical technique, improved perioperative care and more multidisciplinary co-management. It is less straightforward to conceptualize or measure how academic status in itself might be associated with outcome independent of hospital volume. This highlights the importance in any volume-outcomes research to sequentially control for covariates that might partially explain any observed association between volume and outcome. This is critical because it can provide insight into the reasons why higher volume hospitals (or academic hospitals) have better outcomes and thereby creates a model to improve outcomes at low- and medium-volume centres. The alternative is to consolidate all care at high-volume hospitals which may not be feasible, practical, or desirable and needs to be balanced against the very real risk of reduced access to care. This issue has been nicely explored by Elting and colleagues in their study of all cystectomy cases in Texas during 1999–2001.5 Although unadjusted postoperative mortality was lower in high-volume hospitals they discovered that much of the association was explained by differences in the nurse-to-patient ratio such that good outcomes could also be achieved in lower volume hospitals with higher staffing ratios. Management of MIBC is complex and best managed by a multidisciplinary team. In addition to maximizing the effectiveness of cystectomy in routine clinical practice, efforts are required to improve uptake of perioperative chemotherapy and ensure that patients who are not candidates for cystectomy are considered for radical radiotherapy which also offers the chance of long term survival. It is imperative to understand how quality and processes of care can be maximized to close the efficacy-effectiveness gap and improve patient 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)0.147
metaresearch head score (Gemma)0.434
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: Commentary · Consensus signal: none
Teacher disagreement score0.147
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1470.434
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0030.004
Science and technology studies0.0020.004
Scholarly communication0.0100.010
Open science0.0030.010
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.267
Teacher spread0.246 · 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
GenreCommentary

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
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Urological Association Journal→Same topicBladder and Urothelial Cancer Treatments→French-language works237,207→