The effects of immunotherapy and novel therapies on medical oncology work load in a Canadian province.
Bibliographic record
Abstract
6532 Background: Both novel targeted therapies and immunotherapies have dramatically changed the landscape in a number of disease sites with previously limited treatment options. This has resulted in an impact on clinical workload for oncologists with subspecialty practices in the areas of non-small cell lung, (NSCLC), melanoma (M), and genitourinary (GU) cancer. Our aim was to investigate the shift in workload amongst these practices as compared to other disease sites within a single academic cancer center in Nova Scotia (NS), Canada. Methods: The NS Cancer Center is the academic cancer center for the province of NS providing consultative and ongoing care for approximately 72% of provincial patients. We manually quantified appointment visits (new consultation, treatment and follow up visits) as well as telephone toxicity and chart checks booked from February 1 to April 30 across a 3-year interval (2016, 2017, and 2018) and then extrapolated this data to derive full year estimates. Disease sites most impacted by therapies that have changed treatment landscape (NSCLC, M and GU) were compared with the Breast and Gastrointestinal disease sites. Results: Clinical workload increased across all domains over the 3 year period but the majority of the increase is attributed to the 3 disease sites (Table). Conclusions: Medical oncology workloads are increasing over time and novel treatments (including immunotherapy) in disease sites with previously limited options likely account for a significant portion of that increase. New patient consultation metrics, taken in isolation, do not reflect current trends in medical oncology workload. Hiring practices, space allocation and use of physician extenders must take into account these shifting workload dynamics to mitigate physician burnout and potential impacts on quality and timeliness of care. [Table: see text]
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".