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COVID-19 impact on diagnosis and staging of colorectal cancer: A single tertiary Canadian oncology center experience.

2022· article· en· W4206368364 on OpenAlexaffabout
Mathias Castonguay, Corentin Richard, Marie‐France Vachon, Rami Nassabein, Danielle Charpentier, Mustapha Tehfé

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineColonoscopyPandemicColorectal cancerGeneral surgeryGrading (engineering)Coronavirus disease 2019 (COVID-19)Pathological stagingCancerSurgeryInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

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33 Background: COVID-19 pandemic urged public health to imposed drastic reduction on endoscopic activities and surgery, leading to delays that still not have been caught up today. The Ministry of Health and Social Services (MSSS) of Quebec conducted a study of the impact of those measures and reported a 66% reduction of colonoscopy and a 30% reduction of colorectal cancer (CRC) surgery activities during the first wave (March to May 2020). Whether those reduction had an impact on diagnosis and staging of CRC remains unknown. Methods: Demographic information of CRC diagnosed at Centre Hospitalier de l’Université de Montréal (CHUM) between January 1 2018 and March 12 2020 (pre-pandemic period), and March 13 2020 and June 30 2021 (pandemic period) were obtained from the SARDO registry and data regarding colonoscopy, surgery and staging at diagnosis (clinical or pathological as appropriate) were collected. Priority of elective colonoscopy was defined using the MSSS grading system ranging from P1 to P5. We compared delays to colonoscopy, delays to surgery and CRC staging of the pandemic period to the pre-pandemic period using one-way ANOVA, t tests and Chi-square tests as appropriate. Only delays in elective surgeries intended as first and curative treatment were analyzed. Results: 280 CRC diagnosis were made at the pre-pandemic period compared to 127 CRC diagnosis during the pandemic period. Mean diagnosis rates of the pandemic period tend to be lower (8.3 vs. 10.5 diagnosis/month, p=0.03) compared to the pre-pandemic period. 37.6% of patient in the pandemic period had a diagnosis of CRC during a hospitalization compared to 25.9% at the pre-pandemic period (p=0.048). 51.7% of elective colonoscopy leading to a diagnostic of CRC during the pandemic period did not meet required delays according to priority compared to 38.3% (p=0.049) during the pre-pandemic period. P3 colonoscopies (mostly indicated for a positive FIT and iron deficiency anemia) were the most affected (58.9 vs. 106.5 days, p˂0.001). P2 colonoscopy (indicated for suspected colorectal cancer) did not experienced an augmentation in delays (20.9 vs. 25.2 days, p=0.39). A mean of 3.5 elective curative surgeries per month were performed during the pandemic period compared to 3.4 at the pre-pandemic period (p=0.96), and mean delays for surgery were not affected (60.4 vs. 57 days, p=0.59). Stages at diagnosis did not differ (p=0.2). Most of the delayed colonoscopies led to a stage 0 or I CRC and did not lead to a higher stage at diagnosis (p=0.2). Conclusions: In our center, the COVID-19 pandemic led to overall less CRC diagnosis and increased diagnostic endoscopic delays without a higher rate of advanced stage disease. Delays for elective surgeries were quite similar once the CRC diagnostic established.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.052
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.234
GPT teacher head0.563
Teacher spread0.330 · 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".

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Citations2
Published2022
Admission routes2
Has abstractyes

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