The impact of face-to-face evaluation in the clinical assessment of cancer patients.
Bibliographic record
Abstract
e13651 Background: Despite the growing popularity of teleoncology in the context of the COVID-19 pandemic, there is great concern about the quality and accuracy of clinical encounters as there is virtually no physical examination (PE) provided. Objectives: To evaluate the primary sites and clinical variables that could predict a higher frequency of abnormal PE findings and a consequent change in conduct, thus allowing the selection of patients who are possibly ineligible for remote care. Methods: Observational and prospective study, conducted at the oncology center of two public hospitals in Brazil. A multiple-choice form, developed by the researchers, was filled in to collect the clinical variables of the patients, data regarding the service, and oncological conduct determined at the end of the consultation. Results: 368 clinical evaluations of patients with cancer were included in the study, with breast, colorectal, and prostate tumors being the most frequent. The physical examination was normal or with alterations already seen in previous consultations in 87% of the cases while 13% had new changes in the PE. Among patients with new changes in the PE, cancer treatment was maintained in 59% of cases. Thus, PE led to a change in antineoplastic therapy in 12 cases (3% of all patients being 1% only because of the PE findings and 2% after completion of a complimentary examination). Statistical analysis showed a significant association between the altered PE and the following variables: primary site breast (n=27; 21%; p<0.01), head and neck (n=7; 26%; p<0, 01) and ovary (n=2; 17%; p<0.01), presence of symptoms (n=31; 21%; p<0.01), and treatment with palliative intent (n=23; 19%; p<0.01). However, only the presence of symptoms showed a positive association with the altered PE and consequent treatment change (p<0.01). Conclusions: Telemedicine seems to be a safe modality in the vast majority of cases, given the large percentage of asymptomatic patients with no changes in the PE during face-to-face care. Regarding symptomatic patients, we suggest priority to in-person care. In those with EC IV tumors undergoing palliative treatment or with breast, head and neck, and ovarian cancer, we recommend evaluating case by case and considering face-to-face care.
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 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".