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Record W4411180088 · doi:10.3390/jcm14124106

Unique Considerations in Caring for Rural Patients with Rectal Cancer: A Scoping Review of the Literature from the USA and Canada

2025· review· en· W4411180088 on OpenAlexaboutno aff
Lydia Manela Rafferty, Bailey K. Hilty Chu, Fergal J. Fleming

Bibliographic record

VenueJournal of Clinical Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsnot available
FundersMedical Center, University of RochesterStrong
KeywordsMedicineColorectal cancerCancerGeneral surgeryIntensive care medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Background: Rural patients, including those with rectal cancer, continue to be underrepresented in research and medically underserved with unique challenges to accessing care. Like the rest of America, rural patients are experiencing rising rates of rectal cancer; however, unlike the rest of the country, they also have rising rectal cancer-related mortality. This study aims to review the literature regarding care for patients with rectal cancer in rural settings, from presentation and diagnosis to treatment algorithms, oncologic outcomes, their unique preferences, and the goals of care. Methods: A literature search was performed on PubMed, on 31 October 2024, using synonyms of “rural” and “rectal cancer” to identify relevant articles. Articles from outside the USA and Canada and those offering only commentary were eliminated during the initial screening/retrieval. A full-text review was performed on the remaining articles; all the studies that did not address the identified primary or secondary outcomes in rural rectal cancer patients were then excluded. All the primary and secondary outcomes are briefly summarized in narrative form, with more detail on the primary outcomes provided in tables. The variability in the key criteria between the studies is also summarized in the tables and appendices provided. Results: Thirty studies were identified that addressed the outcomes of interest in rural rectal cancer patient populations. The total number of participants could not be assessed given the use of overlapping databases. Of the articles, 21 addressed treatment modalities (surgery, chemotherapy, radiation), 13 addressed oncologic outcomes, and a mix of additional studies addressed the diagnostic work up, costs, and patient preferences. The studies addressing treatment demonstrated similar practices in regard to chemotherapy and surgical management, aside from lower rates of minimally invasive surgery, along with decreased neoadjuvant radiotherapy use and increased under-dosing in rural patients. The oncologic outcomes were overall similar to worse for rural patients as compared to urban patients, even for those receiving treatment at high-volume urban centers. Additionally, rural patients have higher healthcare costs for rectal cancer care. Discussion/Conclusions: Rural patients are an at-risk group, with a rising disease burden and worsening rectal cancer outcomes, despite advances in rectal cancer care and improving oncologic outcomes in the general population. Analysis of the situation is complicated due to the underrepresentation of rural patients in research and the lack of uniformity in the definition of “rural”. Moreover, significant gaps in the literature remain, such that the evaluation of guideline-concordant care is incomplete, including an absence of literature about watch-and-wait approaches in rural populations. While regionalization of rectal cancer care has shown promise, the improvements in outcomes may not be commensurate for rural patients. Thus, a specific focus on the impact of this shift for rural patients is necessary to mitigate unintended consequences.

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)0.012
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.671
Threshold uncertainty score0.655

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.061
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0300.037
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0020.003
Research integrity0.0020.001
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 head0.077
GPT teacher head0.458
Teacher spread0.381 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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