Poster - Thur Eve - 52: A Web-based Platform for Collaborative Document Management in Radiotherapy
Bibliographic record
Abstract
We describe DepDocs, a web-based platform that we have developed to manage the committee meetings, policies, procedures and other documents within our otherwise paperless radiotherapy clinic. DepDocs is essentially a document management system based on the popular Drupal content management software. For security and confidentiality, it is hosted on a linux server internal to our hospital network such that documents are never sent to the cloud or outside of the hospital firewall. We used Drupal's in-built role-based user rights management system to assign a role, and associated document editing rights, to each user. Documents are accessed for viewing using either a simple Google-like search or by generating a list of related documents from a taxonomy of categorization terms. Our system provides document revision tracking and an document review and approval mechanism for all official policies and procedures. Committee meeting schedules, agendas and minutes are maintained by committee chairs and are restricted to committee members. DepDocs has been operational within our department for over six months and has already 45 unique users and an archive of over 1000 documents, mostly policies and procedures. Documents are easily retrievable from the system using any web browser within our hospital's network.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.116 | 0.077 |
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".