The Safeguard measures for mitigating the impact of COVID-19 on radiotherapy services in a Cancer Hospital: A resource-constrained approach
Bibliographic record
Abstract
This article suggests the preventive measures for healthcare department (particularly radiotherapy department) to reduce the probability of corona virus transmission with a resource constrained approach without affecting the work flow. COVID-19 has affected the patients as well as staff of radiotherapy department leaving a severe negative impact on the financial resources of INOR cancer hospital, Abbottabad. Multiple preventive measures have been taken to reduce the probability of spreading the coronavirus while pursuing the timely treatment of radiotherapy patients without compromising their oncological outcomes. In this context, a triage center was established to filter out the Covid suspected/confirmed patients to reduce the risk of infection to other patients and staff. Social distancing was ensured by making amendments in patient gathering areas. Also extensive ventilation and disinfection procedures were adopted to clean the surfaces. Following these measures, patient flux did not show any considerable decrease in second, third and fourth wave as compared to first wave when patient flux reduced to about less than 25 %. Preventive measures were also taken for the employees by ensuring them to wear personal protective equipment during office hours. To further reduce the probability of contact, telemedicine was adopted for patients where possible. All employees were made to be fully vaccinated by July 2021 resulting in 100 % reduction in new cases among INOR employees in the following fourth COVID wave. Owing to these stringent measures taken to fight against coronavirus, ratio of contracting the coronavirus among the employees and patients of INOR has been found <10% overall in this pandemic, While no mortality has been reported so far.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".