Notice bibliographique
Résumé
I am an anesthesiologist and Director of a chronic pain clinic in an academic centre in southwestern Ontario. Over the past 4 years, I have seen our waiting lists grow to 6 to 9 months and longer. In some pain clinics across Canada, the waiting list is 2 years. Understandably, this is causing widespread dissatisfaction. The longer someone suffers from pain, the less likely it is that they will have a successful return to work and function. Two years ago I was able to see six new patients a week; now it is down to three, with almost no time available for urgent requests. Although there are several reasons for burgeoning waiting lists, including the increased demands of an aging population and the retirement of pain specialists, an important part of the problem is the unwillingness of family doctors to take patients suffering from chronic pain into their practices. At our clinic, we no longer accept patients who do not have family doctors. But of the approximately 270 patients on my active roster, 40 do not have family doctors. How has this happened? It has become an almost weekly occurrence to hear of a family doctor quitting practice for such reasons as retirement, illness, moving, or changing to a less stressful type of practice. My heart sinks when patients divulge this, knowing that I have become de facto the family doctor. If they are lucky enough to locate a physician who is considering taking on new patients, they will usually fail the “screening interview.” In this process, anyone with fibromyalgia or back pain is turned down, especially if they are taking opioids. I have even had a young patient taking acetaminophen with codeine (Tylenol 3) for a first-time acute disk herniation who was refused by three family doctors. Another serious problem is that some patients who are lucky enough to have family doctors continue to need follow up at the pain clinic because their doctors refuse to prescribe opioids, even when sanctioned by a pain specialist. Recently, the Ontario Liberal government has focused on decreasing waiting lists for cancer care, joint replacements, and cardiac surgery. This type of work requires highly trained specialist teams, and it is, therefore, difficult to reduce waiting times quickly. In chronic pain management, however, with a modest amount of education, family doctors could develop the skills to continue on with prescribed medications. This would have a direct effect in reducing waiting times for pain clinics across the province. I understand that these patients are very time-consuming and have many complaints and comorbidities. As yet, there is no fee code for complex chronic pain, and this needs to be addressed. My Clinical Research Assistant, Jana Moulin, and I would welcome hearing proposed solutions to the problem of insufficient primary care for chronic pain patients.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,258 | 0,067 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».