The Digital Divide: A Retrospective Survey of Digital Rectal Examinations during the Workup of Rectal Cancers
Bibliographic record
Abstract
Background: Digital rectal examination (DRE) is considered an important part of the physical examination. However, it is unclear how many patients have a DRE performed at the primary care level in the work-up of rectal cancer, and if the absence of a DRE causes a delay to consultation with a specialist. Methods: A retrospective patient questionnaire was sent to 1000 consecutive patients with stage II or stage III rectal cancer. The questionnaire asked patients to recall if they had a DRE performed by their general practitioner (GP) when they first presented with symptoms or a positive FIT test. Demographic data, staging data, and time to consultation with a specialist were also collected. Results: A thousand surveys were mailed out, and a total of 262 patients responded. Of the respondents, 46.2% did not recall undergoing a digital rectal examination by their primary care provider. Women were less likely to undergo a DRE than men (28.6% vs. 44.3%, p = 0.019). While there was a trend towards longer times to specialist consultation in patients who did not undergo a DRE (27.0 vs. 12.2 weeks), this was not statistically significant (p = 0.121). Conclusion: A significant proportion of patients who are FIT positive or have symptomatic rectal bleeding do not recall having a DRE by their primary care provider. Barriers may include lack of comfort with performing DRE or lack of time. Clearer guidelines and more support for GP’s may increase uptake of DRE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".