Impact of Delay in Healthcare Access on Anal Cancer Diagnosis
Bibliographic record
Abstract
AIMS: COVID-19 pandemic caused a significant disruption in healthcare services, leading to a reduction in routine check-ups as well as a shift towards virtual care. This resulted in many patients delaying or avoiding seeking medical attention for symptoms, which led to delay in diagnosis, especially for conditions such as anal cancer that require a physical examination and diagnostic endoscopy. This study aimed to highlight the impact of healthcare disruptions and resultant impact on advanced stage of anal cancer diagnosis. MATERIALS AND METHODS: This is an audit of all patients at a large academic centre who presented with anal canal squamous cell carcinoma between 2018 and 2022. Time/year of presentation, tumour size, presence of nodal and metastatic disease, and primary treatment at the time of presentation was collected for analysis. Kruskal-Wallis, Fisher-Freeman-Halton, and Chi-square tests were used as statistical measures to compare tumour sizes and overall stage. RESULTS: One hundred forty-five patients with histological diagnosis of anal canal squamous cell carcinoma were seen between 2018 and 2022. A significantly higher proportion of patients presented with locally advanced, nodal, or distant metastatic disease in the years during and after COVID-19 healthcare delivery disruption (1/4/2020-31/3/2023). In the years post-COVID-19, a higher proportion of patients were diagnosed with metastatic disease at presentation. Furthermore, patients were more likely to be treated with palliative intent radiotherapy and chemotherapy in later years of healthcare disruption compared to years prior to restrictions placed by COVID-19. CONCLUSION: This data suggests that changes in patient messaging and limited healthcare access during the COVID-19 pandemic negatively impacted the presentation of anal cancer patients. After the onset of the COVID-19 pandemic, disruptions in normal patient care led to patients presenting with more advanced disease and specifically metastatic disease.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".