"Prefer clinical application questions over pure recall when the content
allows" produced sets of vignettes — recognition without understanding.
Both generators now ask for an even split: about half clinical, a child
in front of you and what to do next, and about half mechanism, why the
body behaves as it does. Pathophysiology is what makes the clinical half
stick.
The question generator is also told outright that the patient is a child
and that the ages, doses and norms are the paediatric ones. Its opening
line said "pediatric medical education expert" and left the rest implied.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
"PREP" is the American Academy of Pediatrics' trademark for their own product.
The plans here are our own sets of questions grouped by year, so they are now
named for what they are: Board Review 2021, and Mixed Review for the plan that
draws from every year at once.
Renamed in the database as well as the code — 13 plans, 14 quizzes a learner had
already generated from a block, and the 12 year tags, which appear in the
question bank's filters and are as visible as the plans. The seeder matches both
the old and new names so a fresh import still finds its material, and the tagger
mints the new one so the next run cannot undo this. Prompts and comments that
described the source PDFs by that name now describe them by what they are.
The generation run's 377 failures were not a bug
Every call was reserving the model's full 64k output ceiling, and OpenRouter
refuses the whole request when the balance is below the reservation — "you
requested up to 64000 tokens, but can only afford 52017" — however short the
answer would actually be. `_call_model` now takes a max_tokens, and the article
writer asks for 4000, which is comfortable for three views of one topic and
keeps each request small enough to be affordable. 98 articles were written
before the balance ran down; 158 exist in total.
Generation is paused at the user's request while credits are topped up.
208 backend, 243 frontend green.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TqXevQJhxFrM7jJg82cgZN
ai_decide now samples 4 points across the section (start, 1/3, 2/3, end)
instead of just the first 30 + last 20 pages. This gives accurate strategy
detection on large documents where the answer format might be deeper in.
New ai_answer extraction mode:
- Extracts questions from Q&A-format PDFs that have no answer key
- AI picks the correct option from each question's choices
- Generates explanation using document context + medical knowledge
- Useful for PDFs like practice tests where answers were never included
- Available manually and as an ai_decide strategy
Flashcard decks can now be renamed:
- PATCH /flashcards/{deck_id} updates title
- Inline edit on FlashcardsPage with responsive layout (input full-width,
buttons wrap under it so Cancel never overflows the card)
- Title truncates with ellipsis when not editing
Note: generate mode (textbook -> MCQs) is unchanged per user request.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
New feature: generate flashcards from PDF sections using AI, completely
separate from the existing quiz system.
Backend:
- FlashcardDeck + Flashcard models with cascade deletes
- flashcard_tag_links table for tag classification (reuses question_tags)
- /api/flashcards/ router: CRUD for decks, browse/search cards, tag filtering
- generate_flashcard_deck Celery task with chunked processing + progress
- FLASHCARD_PROMPT in extraction_modes.py (15 cards per chunk)
- "flashcard" added to admin model task types
Frontend:
- FlashcardsPage: deck grid + card browser with search/filter
- FlashcardStudyPage: flip cards, mark known/review, keyboard nav,
shuffle, progress bar, completion screen
- DocumentDetailPage: "Create Flashcards" button alongside "Extract Quiz"
- Navbar: Flashcards link
- AdminPage: flashcard in model task dropdown
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Extraction modes (no restart needed — code ready for next Celery deploy):
- New QuizCreate.extraction_mode field: standard|questions_only|two_step|regex|ai_decide
- extraction_modes.py: independent implementations that don't touch standard path
- questions_only: extract Q+options, correct_answer="PENDING" for manual fill
- two_step: separate answer key section scan + phase1/2/3 matching
- regex: AI detects answer pattern, generates regex, applies to full doc
- ai_decide: AI reads samples from start+end and picks strategy
- DocumentDetailPage: Extraction Mode dropdown with description per mode
- quiz_tasks.py: routes to correct mode, standard path completely unchanged
Database:
- Deleted 11 orphaned questions from PREP 2013 extraction (quiz 12 was already deleted)
- 268 questions remaining (all PREP 2012)
UI fixes:
- Nextcloud section in Settings now only shown to moderators/admins
(regular users can't upload PDFs so they don't need Nextcloud)
- Upload PDF already hidden in navbar for non-moderators (confirmed correct)
- Resume quiz: now async — study mode quiz data loaded BEFORE showing quiz
so correct_answer is available immediately for feedback
- Resume saves and restores voice selection
- voice field added to ProgressSave schema and Redis storage
- Progress save dependency includes selectedVoice
Attempts:
- POST /attempts/start: reuses existing incomplete attempt by default (fresh=false)
Co-Authored-By: Claude Sonnet 4.6 (1M context) <noreply@anthropic.com>
