Diagnostic Odysseys of New Daily Persistent Headache
Bibliographic record
Abstract
The term diagnostic odyssey refers to patients' difficult journeys to obtain a diagnosis, particular in the case of rare or contested illnesses. This paper describes the diagnostic odyssey of patients with New Daily Persistent Headache (NDPH), a rare headache disorder notable for its persistence, resistance to treatment, and impact on quality of life. Studies conducted to date have taken a medical perspective focusing on characterizing NDPH as opposed to patient construction of the illness experience. This study fills that gap by analyzing 17 semi-structured interviews with participants with NDPH, using the McGill Illness Narrative Interview (MINI). Their narratives detail significant moments of headache onset and when they realized that their condition was called NDPH. Thematic analysis identified themes including relief and devastation upon receiving a diagnosis, the importance of headache as singular, negative and positive experiences with providers, the impact of online support group, choosing friends with complex medical conditions, the cyclical nature of treatment, and maintaining hope. Participants utilized social media and social networks to navigate their illness experiences. Numerous participants also reported negative experiences with providers, which created opportunities for reinforcing identity through social networks, and cyclical interactions when medicalization of their condition was necessary for access to treatments and services. This paper advances narrative bioethics scholarship on diagnostic odysseys by arguing that social networks and cyclical engagement with medical systems support patient autonomy in terms of decision-making around uncertain treatments. It concludes with implications for physician-patient relationships that create epistemic justice for all patients, including patients with NDPH.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".